Introduction
Tropospheric ozone (O3) is the third most important greenhouse gas
. It influences the atmospheric radiative forcing as one
of the main absorbers of infrared and ultraviolet radiation
. It also has a strong effect on human health and
vegetation. High levels of O3 concentrations increase pulmonary and
chronic respiratory diseases, increasing human premature mortality
. High concentrations of O3 reduce
photosynthesis and other important physiological functions of vegetation
. Due to its relatively long lifetime (∼ 2 weeks in
the troposphere), the global variability of tropospheric ozone is the
combination of the complex interactions between anthropogenic emissions,
chemical production and destruction, long-range transport, and
stratosphere–troposphere exchanges. A global increase in tropospheric ozone
has been documented during the last 30 years , the cause of
which is not yet well understood . To determine the
origin of this trend, it is important to evaluate the relative contributions
between natural variability and anthropogenic forcing.
Among the natural forcings, the El Niño Southern Oscillation (ENSO) is an
atmospheric phenomenon with a large-scale circulation pattern that influences
the O3 distribution with a periodicity of
about 2–7 years. ENSO refers to two events in the tropical Eastern Pacific:
El Niño (anomalously warm ocean temperatures) and La Niña
(anomalously cold ocean temperatures). ENSO is the dominant source of the
tropical Pacific variability for the atmosphere and the ocean
. During ENSO, changes in sea-surface
temperatures (SSTs) in the Pacific Ocean have a large influence on the normal
atmospheric circulation, displacing the location of convection and its
intensity . These changes in circulation impact the
temperature and moisture fields across the tropical Pacific, influencing the
chemical composition of the troposphere (;
, Fig. ).
Convection during ENSO affects tropical tropospheric O3 in two ways.
First, convection impacts the vertical mixing of O3 itself. Convection
lifts lower tropospheric air masses with a low ozone concentration, where
O3 lifetime is shorter, to upper troposphere where O3 lifetime is
longer . Overall increased convection leads to a decrease
in the tropospheric ozone column (Fig. a). Second, convection
affects vertical mixing and vertical distribution of O3 precursors
. El Niño events coincide with dry conditions
generating large-scale biomass burning in Indonesia .
During El Niño, TCO over Indonesia is higher than average. A remarkable
change in the tropospheric O3 concentration due to El Niño occurred in
the western part of Pacific during 1997–1998, with an increase in the TCO of
+20 to +25 DU . Atmospheric particulates and O3
precursors increase in Indonesia (Fig. b). During La Niña
events, dry conditions are located in South America, causing an increase of TCO
in the eastern Pacific Ocean (Fig. c).
Schematic of the Walker circulation over the Pacific
ocean. (a) During normal conditions: trade winds induce subsidence
along South America with intrusion of O3-rich air. The TCO is elevated. In
addition, along Indonesia, warmer waters generate convergence that results in
low O3 concentrations. The TCO is weakened (b) during El
Niño events: easterly trade winds are weakened. Therefore, convergence
areas are located near the coast of South America while subsidence zones are
located in the Indonesia. Low TCO is located over the Pacific ocean while
high TCO is located over Indonesia, and (c) during La Niña
events: during exceptionally strong trade winds the convergence over the
Indonesia is stronger. The TCO has the lowest value. Subsidence over South
America brings air masses with high O3 concentration, resulting in higher
TCO than average values.
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Previous studies have characterized the variations of the tropical
tropospheric O3 linked to ENSO . To characterize the
ENSO amplitude several ENSO indices have been proposed based on ENSO
footprints on the pressure field or the outgoing longwave radiation
. developed such an index
for Ozone, the Ozone ENSO Index (OEI), to better characterize the effect of
the oscillation on the O3 distribution and as a diagnostic tool for
tropospheric chemistry models.
A detailed analysis of the effects of convection on tropospheric O3 has
been prevented so far by the paucity of observations
. The restricted number of ozonesonde
observations limits analysis of the links between O3 and ENSO
. Satellite observations can give more information on
the O3 variability, and their global coverage gives better insight into the
processes involved in ENSO . To derive tropospheric O3
several studies have combined ozone measurements from the Ozone Monitoring
Instrument (OMI) that measures the total ozone columns, and the Microwave
Limb Sounder (MLS) that provides vertical ozone profiles in the upper
troposphere and stratosphere. subtracted the stratospheric
column O3 (of MLS) from the total column O3 (of OMI) to obtain the
tropospheric column O3 (named hereafter OMI-MLS). They show a large impact
of ENSO on tropospheric O3 in the tropics by analyzing the OMI-MLS data
. The O3 sensitivity to ENSO was also studied with the
tropospheric emission spectrometer (TES) observations . They
studied, during El Niño, the long-range transport of Asian pollution due
to the Northern Hemisphere subtropical jet. MLS and TES data were also
compared to a chemistry–climate model to study how ENSO can influence the
O3 distribution . These studies demonstrate that the link
between O3 and ENSO becomes a key element of the chemistry–climate
interactions.
The combination of OMI and MLS measurements allows insights into the links
between tropospheric O3 and ENSO, but has limitations because the
tropospheric partial O3 columns are obtained as a difference between two
large quantities, the total column and the stratospheric column. Hence,
possible bias and errors in MLS and OMI data can be amplified when the
partial tropospheric column is calculated. The objective of the present study
is to obtain direct evaluations of tropospheric ozone using assimilation of
ozone profiles from MLS and from IASI.
The IASI instrument, launched onboard MetOp-A in 2006, was designed for
numerical weather predictions and atmospheric composition observations
. IASI allows a daily global coverage at very high
spatial resolution (12 km for nadir observations). Because of its spatial
coverage, the day and night retrieval coverage, IASI provides an important
added value with respect to other satellites like TES or OMI
. The IASI mission is meant to last
for several decades (MetOp) whereas the instruments OMI, MLS and TES are
scientific missions with limited lifespan. Tropospheric O3 from IASI has
been already studied and validated. The IASI ozone data were found to be
particularly well suited to the study of O3 variations in the upper troposphere
. Since we already have about 10
years of data, the IASI mission provides a valuable dataset to study the
O3 variability and trends , both in the
troposphere and the stratosphere
.
More recently, the tropospheric O3 variability due to ENSO has been
studied using 8 years (January 2008 to March 2016) of IASI measurements
. They have shown that IASI retrievals can capture the
variability of tropospheric ozone related to the large-scale dynamical modes
of ENSO.
By assimilating IASI data within the MOCAGE model , we
expect to obtain O3 distributions consistent with OMI-MLS observations and
to have additional information on the vertical O3 distributions in the
troposphere. We use the MOCAGE chemistry transport model (CTM) to assimilate
tropospheric ozone profiles from IASI and stratospheric profiles from MLS
with a 4D-Var (4-dimensional-variational) algorithm. The joint assimilation
of IASI and MLS data was already found to improve modeled O3 in the UTLS
. Since the information in IASI retrievals is
strongly weighted in the troposphere, the assimilation of MLS allows the
introduction of complementary information in the case of
stratosphere–troposphere exchanges , which intensify over
the eastern Pacific Ocean during the La Niña phase of the ENSO. We will
evaluate in this study the relative importance of assimilating MLS and IASI
in the context of the O3 variability related to ENSO. To compute ozone
tendencies MOCAGE uses the latest version of the linear ozone chemistry
parametrization of (CARIOLLE Scheme,
).
The influence of ENSO on tropical tropospheric O3 has been simulated by
CTMs or by global chemistry–climate models
. Fewer studies used
data assimilation to study the distribution and interannual variability of
tropospheric ozone in the Pacific . Data
assimilation allows time series of chemical fields that integrate
all available information from measurements and models to be obtained. This can be
particularly useful when tropospheric retrievals from satellite measurements
become very sparse, due for instance to the occurrence of convective clouds
in the tropical region. Furthermore, the assimilation of IASI data for a long
time period has not yet been considered. The 6-year reanalysis
(2008–2013) of tropospheric O3 that we have computed in the present study
is ideal for studying the ozone variability in the tropics from short-term to
interannual timescales.
The format of this paper is as follows. In Sect. 2 we describe the
observations used for assimilation and model validation, as well as the settings
used by the MOCAGE model and the assimilation suite. In Sect. 3 we discuss
the results obtained assimilating IASI and MLS data, with an emphasis on the
impact of ENSO on tropospheric O3. We derive an Ozone ENSO Index and
compare its evolution to previous studies. The final section summarizes the
results.
Results
We have performed three ozone simulations covering the period 2008 to 2013.
The first simulation, called Direct Model (DM), has been produced by running the
MOCAGE CTM without data assimilation. The model is initialized with a
climatology on 1 November 2007 to allow for a spin-up period of 2 months.
The second simulation, named MLS-a, started in January 2008 with the
assimilation of MLS profiles for the whole period. Finally, the third
simulation (IASI-a) was produced with the assimilation of IASI tropospheric
O3 columns and MLS stratospheric O3 profiles. Both MLS-a and IASI-a are
initialized with the direct model output on 1 January 2008. For the three
simulations the outputs are recorded every 6 hours.
The main results are outlined as follows. Section
contains the validation of the simulations against ozonesondes. The first
validation (Sect. ) has been done considering all the
measurements available in the latitude band between
15∘ S and 15∘ N, providing a statistically significant
validation in the tropical region. The following section (Sect. )
limits the comparison with O3 soundings over the region directly
influenced by ENSO events. In Sect. we analyze the temporal and
spatial variability of TCO during the period 2008–2013. The link between sea
surface temperature and ozone variability is studied with the OMI-MLS
estimations (see Sect. ). The objective is to evaluate how modeled
ozone distributions reproduce the observed ozone variability over the Pacific
ocean during the normal conditions of the Walker cell and during ENSO events.
In Sect. , we compare the Ozone ENSO Index (OEI), computed using the
previous datasets, to the Niño 3.4 index, to demonstrate the added value
of IASI tropospheric assimilation for long-term ozone monitoring. Finally, in
Sect. the vertical distributions of ozone are examined over two
regions (eastern Asia and Indonesia and over the Pacific Ocean) to highlight
the footprint of ENSO within the three model simulations.
Validation with ozonesonde measurements
The equatorial latitudes
The O3 data have been treated as follows: (i) the modeled fields have
been collocated with the soundings in space and time, and (ii) the obtained
values have been averaged on a 2-month basis, in order to take into account
a larger number of soundings for statistical evaluations. The collocation was
done with a linear interpolation along each dimension, which results in a
linear interpolation in time of the model's 6-hourly outputs and trilinear
interpolation in space (on both the horizontal and vertical dimensions).
Figure shows the comparison between the partial ozone column of
the three simulations and the ozonesonde data in the tropical band
(15∘ S–15∘ N). Partial ozone columns (in DU) and relative
differences (in %) are plotted separately for the TCO (1000–100 hPa),
the boundary layer (1000–750 hPa) and the free troposphere (750–100 hPa).
The TCO from ozonesondes (Fig. a) has maxima in summer–fall and
minima in winter. The observed seasonal variation is a consequence of biomass
burning, which provides precursors for ozone formation in summer–fall. The
emission of gases by biomass burning, such as carbon monoxide and
carbonaceous aerosols, intensifies during the dry season (June–July and
August–October) over both the South American and South African regions
. The ozone columns produced by the DM and MLS-a
simulations do not show the variability measured by the ozonesondes; their
correlation coefficients with the sondes data are lower than 0.76
(Fig. a, b). The IASI-a variability matches the ozonesondes better
with a correlation coefficient of 0.88. In particular the IASI-a simulation
exhibits a year-to-year variability that agrees very well with the ozonesonde
data. This is confirmed by the RSD of
the differences between simulated and observed values: the RSD of IASI-a is
6 % whereas it is about 10 % for MLS-a and MD. The relative differences
between simulated and observed values are presented in Fig. b.
IASI-a is less biased (6 %) than DM and MLS-a, and MLS-a has lower biases
(24 %) than DM (32 %). Biases are lower with MLS-a, compared to DM, due
to the assimilation of MLS stratospheric data. The MLS-a improvement is due
to the direct influence of the lowest assimilated level of MLS (170 hPa)
which brings information on the O3 distribution in the UTLS region.
Compared to IASI-a the lower accuracy of DM comes from the use of the
simplified ozone scheme, which does not account for the production of
tropospheric ozone by biomass burning.
Figure c and d show that the IASI-a tropospheric columns are biased
high in the lower troposphere. In this region, the RSDs of the three
simulations are very similar, implying a similar variability compared to
ozonesondes, even if IASI-a matches the ozonesondes slightly better. However,
IASI-a is half as accurate for the boundary layer O3 column than for the
TCO and its biases are higher than MD and MLS-a. Larger biases in the
boundary layer are a consequence of both the low degrees of freedom
of IASI retrievals in the troposphere and the presence of a DM
bias with opposite sign between the free troposphere and the boundary layer.
The positive correction provided by IASI assimilation in the free troposphere
propagates downward in the boundary layer, therefore increasing the original
DM bias.
Ozone concentration and biases of the IASI-a simulation in the free
troposphere (Fig. e and Fig. f) show much better results
than the two other simulations. As can be seen, the sensitivity of the
IASI measurements is larger in the mid- and upper troposphere. The RSD of
IASI-a is around 6 % instead of 11 % for DM and 9 % for MLS-a in the
middle–upper troposphere (Fig. f). The added value of IASI data in
the middle troposphere is particularly remarkable in the case of bias, which
is 2 % for IASI-a instead of 41 and 32 % for DM and MLS-a, respectively.
Since the boundary layer (1000–750 hPa) corresponds approximately to 12 %
of the TCO (1000–100 hPa), the overestimation of the ozone column by IASI-a
does not have a major impact on the TCO used for our study of the ENSO-O3
correlation, which is the main objective of this study.
Time series of partial ozone columns (a,
c, e, in DU) from the IASI-a (red curves), the MLS-a (blue
curve), and the DM (green curve) plotted versus several stations measurements
from WOUDC (black curves). Data are 2-month averages over the area
15∘ S–15∘ N and 180∘ W–180∘ E for
(a), the ozone column between 1015 and 100 hPa (b), the boundary layer (1015–750 hPa) (c),
(d) and the
free troposphere (750–100 hPa) (e), (f). Biases in percentages are shown in
(a), (c) and (e). Mean biases, correlation coefficients
and standard deviations are also given (between brackets in b,
d and f).
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From eastern Africa to South America: focus on the ENSO
To study the ENSO we divide the region of interest (latitude ranges from
15∘ S to 15∘ N and longitude ranges from 70∘ E to
110∘ W) in two areas (see Fig. ): the first one, called
IIO, has a longitude range between 70 and 140∘E while the
second one, called POC, is located between 180 and 110∘ W. Three
ozonesonde stations are available for both regions, two in the IIO region and
one in the POC region (Table ).
Ozonesonde stations at tropical latitudes between
70∘ E and 110∘ W.
Name
Ozonesondes
Localization
Coordinates
Malaysia
443 ECC
Kuala Lumpur international airport, Malaysia
3∘ N–101∘ E
Indonesia
437 ECC
Watukosek, Java Timur, Indonesia
8∘ S–113∘ E
Samoa
191 ECC
Apia, Samoa
13∘ S–172∘ W
Ozone measurements for each site are available over different time periods.
The Malaysia site provides measurements only between January 2008 and
December 2009, the Indonesia site from January 2008 to December 2012, and the
Samoa site from January 2008 to December 2013. Due to the small number of
ozonesonde measurements, results of the statistical validation presented
here should be considered with more caution than in the previous section. The
main objective of this section is to check whether the reanalysis can capture
strong local variations of TCO due to ENSO.
Figure shows the statistics of the IASI-a O3 simulation versus
the three records from the ozone soundings. Time series are computed in the
same manner as the time series over the tropical band discussed in the
previous section (Sect. ). From January 2008 to December
2009, TCOs from the ozonesondes located in the IIO region (Fig. a,
c) and those located in the POC region (Fig. e), has a seasonal
variability, with maxima in boreal summer and minima in boreal winter. This
ozone seasonality is caused by the biomass combustion over the western
Pacific Ocean near New Guinea during the dry period . Among
the countries of southern Asia, Indonesia is known as the country with the
third-highest biomass burning emissions
. During the year 2010 and over the IIO region
(Fig. c), the variability of ozone concentrations has a different
seasonality. We see a peak of ozone during March 2010 over Indonesia (26 DU)
whereas there is a minimum in Samoa (12 DU) (Fig. e). This ozone
rise over the IIO region is linked with subsidence, generated by the El
Niño event starting in January 2010 (see Fig. ). El Niño
intensifies the subsidence and therefore dry conditions and biomass burning
over southern Asia . From September 2010 to
August 2011, the TCO values decrease to an average of about 20 DU over
Indonesia. This decrease in tropospheric ozone is due to the other phase of
ENSO: La Niña. As we have already mentioned (Fig. ), La
Niña strengthens the convection over the IIO causing a minimum in the
TCO. Hence, there is a lower TCO over Indonesia (around 20 DU) than over
Samoa (around 28 DU). After summer 2011 the ENSO disappears and the TCO
returns to normal seasonality.
IASI-a reproduces quite well the variability measured by the ozonesondes
during normal conditions of the Walker circulation (2008–2009) and during
the ENSO (2010–2011). In particular, IASI-a agreement with the ozonesondes
is better over the POC region (Samoa), where the correlation coefficient is
0.96, than over Indonesia and Malaysia where the coefficients are around 0.7.
However, the relative difference between IASI-a and the ozonesondes is larger
over the POC region (Fig. f) than over the IIO region
(Fig. b, d), with an overestimation of the ozone columns by about
17 % in Samoa. Mean biases are around 3–5 % for over Indonesia and
Malaysia, showing that IASI-a reproduces quite well the ozone variability
during normal conditions of the Walker circulation. Equally, IASI-a reproduces
the maximum over Indonesia and the minimum over Samoa during the 2010 El
Niño event, as well as the TCO minima generated during La Niña over
the IIO region. As already discussed, biases observed in the POC and IIO
regions come from the decreased sensitivity of IASI in the boundary layer,
and from the lack of adequate representation of the chemistry in the lower
troposphere by the linear scheme used within MOCAGE. The three simulations
(IASI-a, MD and MLS-a) have identical biases in the boundary layer compared
to the ozone soundings (figures not shown). Biases in the boundary layer are
higher in the POC region (around 45 %) compared to the IIO region (around
20 %). However, in the POC region, the variability of the three simulations
are remarkably well correlated with the ozonesondes, with coefficient
correlations higher than 0.85 (not shown).
Comparisons between IASI-a (in red) and ozonesondes (in
black). Time series of the TCO (in DU) are plotted on the left and relative
differences are on the right for the sites of (a, b) Malaysia,
(c, d) Indonesia and (e), (f) Samoa.
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To summarize, the IASI-a simulation reproduces well the O3 variability
observed with the ozonesondes for the tropical latitudes and for both regions
of POC and IIO. The seasonal oscillations of ozone, caused by the
anthropogenic pollution and by ENSO, are reproduced by IASI-a despite a
slight overestimation of about 4 % in the IIO region and around 17 % in
the POC region. The IASI-a simulation is thus adequate to study ozone
variability during ENSO events since biases are not very large over the
period under study.
Temporal and spatial variability of ozone during ENSO
Characterization of ENSO and footprints on SST and tropospheric ozone content
In this section we consider the link between SST
and tropospheric ozone during ENSO events. Previous studies have highlighted
the link between SST anomalies and ENSO dynamics
. Colder SST in the POC region is
associated with La Niña whereas El Niño has warmer SST than under normal
conditions . Variations in TCO concentrations are a
combination of biomass burning rejecting large quantities of ozone precursors
and an eastward shift in the tropical convection of the
Walker circulation associated with SST changes .
The correlation between SST and TCO have already been characterized using
OMI-MLS data; our objective is to see if similar correlations can be derived
using the model simulations. To this end, we have taken SST data from the
Giovanni Interactive Visualization and Analysis GES DISC: Goddard Earth
Sciences, data and Information Services Centre
(https://disc.gsfc.nasa.gov). The SST data were measured by the
instrument MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the
Aqua satellites (NASA Earth Observing System platforms).
Figure shows the time versus longitude Hovmöller diagram,
averaged between 15∘ S and 15∘ N, of the monthly mean SST
and the OMI-MLS measurements. SST over the Pacific ocean has a characteristic
geographic distribution (Fig. a), with the warmest water in the IIO
region (70–140∘ E) and coldest water in the POC region
(180–110∘ W). The link between SST and TCO is observed comparing the SST (Fig. a) with OMI-MLS
measurements (Fig. b). The warmest water-induced convective
movements result in a TCO decrease and vice versa for the coldest water. During El Niño (January 2010) the warm SST shifts from
the IIO region to the POC region. These eastward shifts in SST coincide with
eastward shifts of TCO from July 2008 to January 2010. During La Niña
(occurred between September 2010 and January 2011) an opposite condition
occurs with the strengthening of colder SST between 80 and 150∘ W.
In this region of colder SST (Fig. a), higher TCO (26–32 DU) is
located between the coast of South America and 140∘ W
(Fig. b). The eastward shift of SST occurring from January 2011 to
December 2013 corresponds to the return of normal conditions over the Pacific
ocean and impacts TCO with an eastward shift.
To compare the three model simulations, with OMI-MLS, we have computed
anomalies of tropospheric ozone of each dataset for the period 2008–2013
(Fig. ). The anomalies are calculated by subtracting the
time-averaged TCO to each TCO determination and this difference has been
divided by the mean TCO. The TCO anomalies are expressed in percentage. The
variability of TCO, observed previously with OMI-MLS measurements in
Fig. , is also clearly visible with the TCO anomaly
(Fig. a). The TCO with values 20 % lower than average are
located in the IIO region. The TCO with values 20 % higher than average are
located close to South American coasts. The El Niño event on January 2010
has a significant impact on TCO, with 20 % higher values in the IIO
region and 10 % lower in the POC region. The La Niña event that follows
shows different localization on the TCO maximum with a maximum between 110
and 80∘ W. Part of the TCO variability in the eastern and western
Pacific Ocean linked with the Walker circulation is reproduced with the DM
simulation (Fig. b). The TCO with values 10 % higher (10 %
lower) than average are located in POC (IIO) region. However, the amplitude
of the TCO anomalies from the DM simulation is much lower compared to
OMI-MLS. Since the chemical scheme used in the MOCAGE model has no
longitudinal forcing in the chemical tendencies, the TCO anomalies in the DM
simulation are only due to changes in the equatorial circulation and the
associated ozone transport. The ECMWF analyses capture the dynamics
associated with the Walker circulation and to ENSO and hence drive the
variations of the TCO seen in the DM simulation. The assimilation of MLS
O3 profiles in the stratosphere does not change the structures and
percentages of anomalies of the TCO much, and the results of the MLS-a
simulation (Fig. c) are similar to those of the DM. The eastward
shift during El Niño and higher TCO in the POC region during La Niña
are represented with both simulations but the TCO anomalies for both
simulations are 10 % lower than with those of OMI-MLS. Basically the ENSO
impacts the troposphere and little information is brought by the assimilation
of MLS data. The eastward shift during El Niño and higher TCO
in the POC region during La Niña are underestimated by both simulations.
Compared to DM and MLS-a the TCO variability is much better reproduced with
IASI-a (Fig. d). The amplitude of the TCO changes caused by El
Niño and La Niña compares very well with the OMI-MLS observations.
However, small differences appear between IASI-a and OMI-MLS. Over the IIO
region during El Niño the TCO anomaly is 10 % lower with IASI-a than
with OMI-MLS. In addition, the location of the TCO maximum during La Niña
is located in the whole POC region with IASI-a and in the eastern part of the
POC region with OMI-MLS.
Overall, the anomalies of the TCO reproduced by IASI-a are in good agreement
with those of OMI-MLS. The improvement compared to the DM and MLS-a
simulations is very significant, thus demonstrating the usefulness of the
IASI data for the ozone evaluation in the troposphere. Equally, the
assimilation process is efficient to follow ozone variability and the
resulting IASI-a analysis appears to give a consistent dataset for the study
of the ozone variability. The advantage of IASI-a over OMI-MLS is that the
analyses are fully four-dimensional with 6-hourly outputs and resolved information on
the vertical dimension. The vertical distributions are studied in
Sect. .
(a) Time versus longitude Hovmöller diagram
of the SST (in ∘C). (b) Same diagram from the OMI-MLS
data. The data are monthly means from January 2008 to December 2013 and
area-averaged between latitudes 15∘ S and 15∘ N. Also
included on the bottom are the corresponding maps of the Hovmöller diagram.
![]()
Longitude Hovmöller diagrams of TCO anomalies for
(a) OMI-MLS measurements, (b) Direct Model,
(c) MLS-a and (d) IASI-a between 2008 and 2013. Longitudes
are identical to Fig. : between 40∘ E and 80∘ W.
Anomalies are expressed in percentage.
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Intercomparison of Ozone ENSO Indices
The OEI is the TCO difference computed between the IIO region
(70–140∘ E) and the POC region (180–110∘ W). The
resulting time series are then deseasonalized. This deseasonalization is done
to remove the signal of the annual cycle . OEI is a strong
indicator of the ENSO intensity influencing the tropospheric ozone over IIO
and POC regions . It is considered as a basic diagnostic
tool to evaluate the ability of the models to reproduce changes in
tropospheric ozone linked with ENSO .
(a) Monthly mean tropospheric Ozone ENSO Index
(in DU) derived from the OMI-MLS data (grey line). Also shown is the
Niño 3.4 monthly temperature anomaly ENSO index (cyan curve, multiplied
by a factor of 3, in Kelvin) and the OEI-Z index derived from the OMI-MLS data
with a deseasonalization followed by a sliding average of 3 months (orange
curve). (b) The OMI-MLS data (grey curve) as in the above plot, the
MLS-a (in blue curve), the DM in green curve and the IASI-a (in red curve).
All ENSO indices extend from January 2008 through December 2013.
![]()
Figure shows the OEI during the period January 2008 to December
2013 computed from our model analyses and from the OMI-MLS data. The OEI
variations are related to ENSO, with maxima during El Niño and minima
during La Niña events. In Fig. a we have plotted the OEI
computed for the OMI-MLS measurements (noted OMI-MLS) and the Niño 3.4
index. The monthly Niño 3.4 is calculated from SST anomalies in the
Pacific Ocean. The Niño 3.4 index calculated from SST is available from
the NOAA website (http://www.cpc.ncep.noaa.gov/data/indices/). Sea
surface temperature anomalies were calculated using the monthly Extended
Reconstructed Sea Surface Temperature version 4 (ERSST.v4, 1950–2016 base
period). Also included is the OEI of OMI-MLS smoothed using a 3-month running
average, as computed by and called OEI-Z
hereafter. Figure b shows the OEI computed from IASI-a, MLS-a, DM
and OMI-MLS. Our OEI indices from OMI-MLS, DM, MLS-a and IASI-a are computed
without time averaging, by subtraction of TCO in the POC region from TCO
averaged over the IIO region. As defined by the NOAA, the two ENSO phases
occur when the Niño 3.4 index is higher than 0.5 (corresponding to El
Niño) and lower than -0.5 (corresponding to La Niña) during five
consecutive months. Thus, in the analyzed period an El Niño starts on
July 2008 with a maximum on January 2010, and a La Niña starts on
July 2010 with a maximum on January 2011. The two time series of OEI-Z and
OMI-MLS appear remarkably similar (Fig. a), except around
January 2008. For this period they are out of phase with the Niño 3.4.
The discrepancy is attributed to the phase opposition between the interannual
and intraseasonal variability of the TCO linked with the
intraseasonal Madden–Julian Oscillation (MJO, ).
The MJO increases the differences between OEI-Z and OMI-MLS in 2008.
Detailing the effect of MJO on monthly OEI is beyond the scope of our current
study. As expected, the OEI from OMI-MLS shows a consistent variability with
OEI-Z; in particular the maxima and minima agree and are well correlated to
the Niño 3.4 index. Since the OMI-MLS OEI is obtained from monthly
averages it exhibits shorter term variability than OEI-Z and can be directly
compared to the indices derived from the model simulations.
The DM OEI (Fig. b, green curve) is negative during the whole
period, corresponding to a tropospheric column higher over the POC region
than over the IIO region. The DM OEI variations show some features of the
ENSO, with a relative maximum in January 2010 followed by a minimum at the end
of the same year, but the intensity is weak: about 3 times lower than
values observed with OMI-MLS. The MLS-a produces an OEI very similar to DM.
As already discussed, constraining the ozone profile in the stratosphere has
little impact on the quality of the modeled ENSO O3 signal. With the
IASI-a we can quantify the contribution of IASI data in the computed OEI
(Fig. b, red curve). Compared to DM and MLS-a simulations the
IASI-a analysis produces OEI in better agreement with the ones derived from
OMI-MLS. The OEI variations are in phase with a very good match of periods of
maxima and minima. There is, however, a constant bias of approximately 2.4 DU
between the indices of OMI-MLS and IASI-a. As discussed in Sect. ,
IASI-a bias in the lower troposphere is larger in the POC region than in the
IIO region. This difference of biases between POC and IIO regions affects the
determination of the OEI. In addition, during ENSO events we have seen from
the Hovmöller plots in Sect. that during La Niña the TCO
maximum with IASI-a is slightly shifted to the western part of the POC region
compared to the OMI-MLS data. The difference in the location of maxima over
the eastern Pacific between OMI-MLS and IASI-a explains part of the
difference in the OEI absolute values during El Niño and La Niña
events (Fig. ).
Tropospheric ozone variability during ENSO is therefore very well captured
from the OEI variations computed from IASI-a, despite a constant bias in the
boundary layer. Further insights into the vertical distribution of O3 over
the POC and IIO regions during ENSO are discussed in the next section.
Vertical structure of O<inline-formula><mml:math id="M150" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:math></inline-formula>
The evaluations of TCO obtained with the OMI-MLS by subtracting stratospheric
ozone from MLS from the total ozone from OMI cannot give information on the
vertical structure of the O3 anomalies forced by ENSO. This is clearly an
advantage of model assimilations that can give a complete three-dimensional structure of
the ozone fields with no gaps due to orbitography and clouds. We focus here
on the information brought by the assimilation of IASI and MLS data in
describing the vertical ozone response to ENSO in the POC and IIO regions.
Figure shows monthly mean ozone profiles for IASI-a, MLS-a and DM,
over the 6-year record. The tropopause pressure for the three simulations
is about 100 hPa. Ozone concentration in this layer is around 70 ppbv. Due
to the limitations of the model and the lack of information brought by the
two instruments in the boundary layer, as already discussed, we focus our
analysis in the IIO et POC regions on the free troposphere, between 750 and
100 hPa.
Monthly mean time series of ozone vertical profiles
(units ppbv) versus pressure for the IIO region (a, c, e) and the
POC region (b, d, f). The abscissa goes from January 2008 to
December 2013. Panels (a, b) correspond to the Direct Model, (c, d)
to the MLS-a and (e, f) to the IASI-a simulations. Pressure scale
goes from 1013 to 20 hPa.
![]()
The DM (Fig. a, b) and MLS-a (Fig. c, d) produce very
close distributions of the vertical ozone concentration. The MLS-a simulation
shows slightly more ozone in the lower stratosphere and upper troposphere,
but the fluctuations of the concentration have similar amplitudes in both
simulations. Particularly noticeable is the signal during the 2010 El
Niño with low ozone values in the POC region during the first months of
the year linked to increased convection and associated upward motions, and an
opposite behavior in the IIO region with subsidence and increased ozone down
to the middle troposphere. This footprint of ENSO is very well captured with
the IASI-a simulation, especially over the POC region. Over that region the
ozone content is lower than 35 ppbv during El Niño and larger than
50 ppbv during La Niña. The information brought by IASI is very
significant, the amplitude of the ozone change between El Niño and La
Niña periods is 2 to 3 times larger with IASI-a assimilation than it is
with DM and MLS-a simulations. If we refer to OEI indices (Fig. )
some ENSO activity is detected in late 2012–early 2013. Indeed an O3
minimum in early 2013 followed by a maximum in the middle of the year is
clearly visible in the IASI-a assimilation in the POC region. The amplitude
of the ENSO signal on ozone is lower than for the 2010 event, in agreement
with the lower values of the Niño 3.4 index. Also more clearly visible
with IASI-a are the seasonal variations of the ozone content in the IIO
region that is quite regular outside ENSO periods. In that region the annual
periodicity of ozone is much pronounced in comparison to the more erratic
variations shown in the POC region. The regularity of the ozone fluctuation
is more pronounced in IASI-a assimilation than in DM and MLS-a simulations.
In addition to the influence of atmospheric dynamics, biomass burning and the
associated ozone production could trigger the seasonal fluctuations. Such an
ozone production detected by the IASI instrument (and therefore visible in
IASI-a) cannot be reproduced by the DM and MLS-a simulations due to the
simplified chemical scheme.
Overall the combination of the IASI ozone tropospheric retrievals and our
4D-Var algorithm produces a very consistent dataset for the study of the
influence of ENSO on the ozone distribution from the stratosphere to the
middle troposphere. The quality of IASI-a, which also includes the
assimilation of MLS, is good in the stratosphere down to the middle troposphere.
In the boundary layer, below 800 hPa, a comprehensive chemical scheme with
adequate emissions should be used to improve the assimilation since there are
no global observations of the ozone content in this layer over the equatorial
regions.
Summary and conclusion
A total of 6 years (from January 2008 to December 2013) of 6-hourly
tropospheric ozone fields have been derived by assimilating IASI and MLS
ozone measurements in the MOCAGE CTM. The assimilation of IASI tropospheric
columns combined with MLS stratospheric profiles was first validated against
ozonesondes in the tropical band (15∘ S–15∘ N), providing
a statistically robust validation. In the tropical band and over the whole
period, IASI-a gives results similar to ozonesondes and reproduces the ozone
variability well despite a constant bias. Biases in the analysis come from
the low accuracy of the model in the boundary layer. The ozone linear scheme
in MOCAGE does not take surface emissions into account. In addition, IASI has
a weak sensitivity in the boundary layer and therefore does not provide
additional information on O3 content in this layer. A
second validation has been done over the Pacific ocean and over southern Asia
(longitude band of 70∘ E to 110∘ W). During the 2008–2013
period, an ENSO event developed with its two phases: El Niño in
winter 2010 and La Niña in winter 2011. IASI-a has been validated in two
areas: the Indonesia and Indian Ocean and the Pacific Ocean Center regions.
In both regions, biases appear and are larger in the POC region. The weak
sensitivity of IASI sounding in the boundary layer is responsible for these
biases. However, the tropospheric ozone variability related to the Walker
Circulation and to the ENSO event is well reproduced with IASI-a.
OMI-MLS tropospheric columns have been used and validated by several past
studies. We have used OMI-MLS ozone data to characterize the links between
SST and tropospheric O3 and to compare with our IASI-a assimilation.
Anomalies of TCO have been computed, allowing a comparison between IASI-a and
the two other simulations (Direct Model and MLS-a) with OMI-MLS. Anomalies of
the Direct Model (MOCAGE without assimilation) are similar to anomalies of
MLS-a (assimilation of MLS stratospheric profiles). The good reproduction of
anomalies in terms of location and timing between eastern and western regions
in both simulations are due to the transport forced by the winds from the
ECMWF meteorological analyses. However, the amplitude of anomalies is lower
than in OMI-MLS data. Assimilation of IASI data corrects this behavior, and
the anomalies of IASI-a appear very similar to the OMI-MLS anomalies. In
particular, the IASI data bring essential information to reproduce the
eastward
shift of TCO caused by El Niño.
In order to study the ability of IASI-a to reproduce the ozone variability
caused by El Niño and La Niña phases, we have used the OEI. The OEI
represents an essential diagnostic test for models that should be able to
represent ozone features linked with ENSO changes in tropospheric dynamics.
OEI from IASI-a shows variations similar to those of OMI-MLS with a small
bias corresponding to higher TCO over the POC than over the IIO region.
The Direct Model and MLS-a have the same bias. This bias has been located in the
boundary layer with the comparison with the ozonesondes.
We have also examined the vertical structures of tropospheric ozone in the
IIO and POC regions, with the three simulations (Direct Model, MLS-a and
IASI-a), in order to show the contribution of IASI tropospheric data in the
assimilation. The IASI-a analysis is consistent with the ozone displacements
in adequation with subsidences and convergences generated by El Niño and
La Niña in both IIO and POC regions. The IASI assimilation gives a very
valuable high-resolution dataset suitable to perform analyses of the O3
variability in the upper and middle troposphere for short-term and
interannual timescales in the tropical band.
Overall, the assimilation of stratospheric MLS and tropospheric IASI data
within MOCAGE gives a good representation of the tropospheric ozone
variability linked with ENSO and the Walker circulation. We have shown the
importance of assimilating tropospheric IASI data to provide vertical
information on tropospheric ozone variability, showing the benefit of IASI
analyses for studies on ENSO dynamics. In addition, since ENSO is one of the
most important interannual fluctuations in climate variability, this study is
part of a climate variability perspective. The assimilation of satellite data
is promising for determining the impact of climate variability on
tropospheric chemistry. There are, however, some limitations in our simulations
that have to be addressed. One of them is the bias found in the boundary layer
over the Pacific Ocean that affects the calculation of the OEI. In this study
we have used a linear ozone parameterization to compute the ozone chemical
tendencies. This approach is suitable for the free troposphere and the
stratosphere but is certainly not adequate for the boundary layers. In the
future we plan to use a more comprehensive chemical scheme that accounts for
the surface emissions.
With the use of IASI data we have demonstrated here the value of assimilating
satellite data that document the direct information in the tropospheric ozone
content to compute OEI. This approach is promising because many types of data
can enter in an assimilation process, such as the balloon and aircraft
measurements. Improvements in the tropospheric ozone content evaluations can
be expected from an increase in assimilated data. Times series of IASI
analysis could then be derived and used to study the tropospheric ozone
variability over at least a 30-year period. One advantage of infrared
sounders like IASI for climate studies is their good spectral stability over
time, with respect for example to UV instruments. This is an important
feature when trying to determine potentially small climate signals hidden by
large ozone variability, due for example to ENSO. Finally, using a more
detailed chemistry scheme within future ozone reanalyses would also allow
further insights into chemical feedbacks in the context of a changing climate.