Introduction
Atmospheric ozone protects all life on earth from damaging solar ultraviolet
radiation. About 90 % of ozone is observed in the stratosphere where it
is strongly influenced by various photochemical and dynamical processes.
Three processes determine stratospheric ozone distribution and concentration,
namely ozone production, destruction and transport. While ozone concentration
in the upper stratosphere (35–50 km) is primary determined by the
processes of photochemical creation and destruction, ozone concentration in
the lower and middle stratosphere (below 30 km) is strongly affected
by transport (Portafaix et al., 2003; Bencherif et al., 2007, 2011;
El-Amraoui et al., 2010). Ozone is formed primarily in tropical regions as
a result of a reaction between atmospheric oxygen and the ultraviolet
component of incident solar radiation; it is then subsequently transported
and distributed to higher latitudes following the Brewer–Dobson circulation
(Weber et al., 2011). The combination of these processes contributes to the
overall spatiotemporal distribution and variability of atmospheric ozone.
The variability in ozone levels also depends on several dynamic proxies and
the chemical evolution of ozone-depleting substances (ODS). Since 1980,
anthropogenic ODS emissions reaching the stratosphere have led to a decline
in global ozone concentration (Sinnhuber et al., 2009). However, recent
observations have indicated that the decreasing trend in stratospheric ozone
has been halted from the mid-90s (1995–1996), and an upward trend has been
observed at 60∘ N–60∘ S since 1997 (Jones et al., 2009;
Nair et al., 2013; Kyrölä et al., 2013; Chehade et al., 2014; Eckert
et al., 2014). This increasing trend in stratospheric ozone is associated
with a decrease in atmospheric chlorine and bromine compounds as a direct
result of the Montreal Protocol which regulates ODS emission (United Nation
Environment Program, UNEP 2009). From the present and going forward, this
increase in stratospheric ozone is expected to begin to be more widely
observable on a global scale (Steinbrecht et al., 2009; WMO (World
Meteorological Organization, 2010, 2014). However, Butler et al. (2016) have
shown that tropical ozone will not recover to typical levels of the 1960s by
the end of the 21st century. Depending on the specific processes involved,
climate change can induce modifications in the stratosphere that can delay or
accelerate ozone recovery. Greenhouse gas (GHG) induced stratospheric cooling
should lead to increased photochemical ozone production and slower
destruction rates via the Chapman cycle (Jonsson et al., 2009). Furthermore,
and with direct relevance to the region under study, acceleration of the
Brewer–Dobson circulation due to tropospheric warming is thought to lead to
decreases/increases in tropical/middle latitude stratospheric ozone.
It is important to create a consistent and reliable dataset in order to
quantify ozone variability, to estimate trends and to validate models used
for predicting future evolution of ozone levels (Randel and Thompson, 2011).
The work presented here investigates the period 1998–2012 where an increase
in stratospheric ozone is expected to be measurable. The aim of this study is
to investigate the current behaviour of seasonal and inter-annual ozone
variability over the southern tropics and subtropics and to analyse whether
the expected ozone recovery is effective in the selected study region and at
which altitude level ozone increase is significant. The WMO (2014) reported
a divergence in ozone profile trends computed from individual instruments in
the lower stratospheric tropical region. Sioris et al. (2014) showed
a declining ozone trend for OSIRIS data, while Gebhardt et al. (2014)
reported an increasing ozone trend using measurements from the SCanning
Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and
Aura Microwave Limb Sounder (MLS) instruments. Therefore, a study of ozone
levels at tropical latitudes and present in the lower and middle stratosphere
is of paramount importance. The trend analysis of two types of ozone data is
relevant with respect to this study, namely, total column ozone (TCO) and
vertical ozone profile measurements. While the TCO analysis offers the best
way to provide information on ozone variability for the complete ozone layer,
profile analysis allows for the investigation of ozone behaviour at different
altitude levels (Nair et al., 2013).
Ozone profile measurement is usually achieved by ground-based lidar
instruments, satellite observation and ozonesondes. However, observation by
ozonesonde is often preferred due to its high vertical resolution. SHADOZ
(Southern Hemisphere Additional Ozonesonde) is the only network which
provides ozonesonde profiles over the southern tropics and subtropics. It has
a precision of approximately 5 % with a vertical resolution between 50
and 100 m (Thompson et al., 2003a). Previously radiosonde
observations from SHADOZ networks have been used to validate tropospheric
ozone observations obtained from satellites (Ziemke et al., 2006, 2011), to
study the stratospheric altitude profile of ozone and water vapour in the
Southern Hemisphere (Sivakumar et al., 2010), to investigate the dynamic
characteristics of the Southern Hemisphere tropopause (Sivakumar
et al., 2006, 2011; Thompson et al., 2012), to study seasonal and
inter-annual variation of ozone and temperature between the atmospheric
boundary layer and the middle stratosphere (Thompson et al., 2003b; Diab
et al., 2004; Sivakumar et al., 2007; Bègue et al., 2010; Mze
et al., 2010) and to analyse the variability and trends in ozone and
temperature in the tropics (Lee et al., 2010; Randel and Thompson, 2011).
In this paper, SHADOZ profiles recorded at eight Southern Hemisphere stations
were used to investigate seasonal and inter-annual variability of ozone and
to estimate the trends at different stratospheric pressure levels. The TCO
measurements were performed based on Dobson and SAOZ (Système d'Analyse
par Observation Zénithale) ground-based instruments (Pommerau and
Goutail, 1988; Dobson, 1931). However, ground-based data were only available
for three of the eight stations considered in this work. Satellite
observations were therefore used in order to complete ground-based
observations, particularly at tropical sites (Nairobi, Java, Samoa, Fiji and
Ascension Island) where ground-based datasets were incomplete or
non-existent. The chosen satellite datasets are OMI (Ozone Monitoring
Instrument) and TOMS (Total Ozone Mapping Spectrometer) (McPeters
et al., 1998; Bhartia, 2002) because of their high-quality ozone data. The
TOMS instrument has facilitated the recording of long-term ozone measurements
on both global and regional scales. TOMS instruments were successfully flown
aboard several satellites from 1978 to 2005, with the latest instrument
aboard the Earth Probe (EP) satellite operational from July 1996 to
December 2005. In the framework of the EOS (Earth Observing System)
programme, TOMS was replaced by the OMI instrument launched onboard the Aura
satellite (July 2004 and operational to the present). The combined TCO data
recorded from ground-based instrumentation and satellites were used to
investigate ozone variability and trends over the selected study region. Data
analysis was conducted using a multivariate regression model known as
Trend-Run (Bencherif et al., 2006; Bègue et al., 2010).
Multivariate regression models are considered powerful tools to study
stratospheric ozone variability and trends (Randel and Thompson, 2011;
Kyrölä et al., 2013; Nair et al., 2013; Bourassa et al., 2014;
Gebhardt et al., 2014; Eckert et al., 2014). In these models, the choice of
proxies is important as the indexes of these proxies are often chosen based
on atmospheric forcings that have been historically accepted as having an
influence on ozone variability (Damadeo et al., 2014). Butchart et al. (2003)
analysed equatorial ozone variability based on a coupled stratospheric
chemistry and transport model. They observed a strong QBO (Quasi-Biennial
Oscillation) signal in total column ozone variability with an observed
correlation between the stratospheric ozone anomaly and the westerly wind
distribution higher than 50 %. Brunner et al. (2006) demonstrated the
existence of a strong QBO signal in ozone variability throughout most of the
lower stratosphere, with a peak amplitude in the tropics of the order of
10–20 %. The ozone–QBO relationship has been discussed in several
papers, as well as ENSO (El Niño–Southern Oscillation) influence on
ozone variability. ENSO events are linked to coherent variations of the ozone
zonal mean in the tropical lower stratosphere, tied to fluctuations in
tropical upwelling (Randel et al., 2009). Randel and Thompson (2011) observed
a negative signal in ozone variability exhibited by the ENSO index with a
magnitude of around 6 % in the lower stratosphere.
It is known that solar flux has also contributed to the timescale variability
of ozone (Zerefos et al., 1997; Randel and Wu, 2007). Several studies have
shown that when the solar activity is high, larger amounts of ozone are
formed in the upper stratosphere. Maximum ozone levels should therefore occur
during periods of high solar activity (Haigh, 1994; Labitzke et al., 2002;
Gray et al., 2010). However, Efstathiou and Varotsos (2013) demonstrated that
the effect of the solar cycle response on total column ozone was caused by
dynamical changes which were related to solar activity. It has been reported
by Austin et al. (2008) that the solar flux response to long-term ozone
variation is around 2.5 %, with a maximum in the tropics. Soukharev and
Hood (2006) have reported that the solar cycle response to ozone variability
is positive and statistically significant in the lower and upper stratosphere
but insignificant over the middle stratosphere. In this investigation, QBO,
ENSO and solar flux proxies were used as inputs in the Trend-Run model to
simulate inter-annual ozone variation over the selected study region. Annual
and semi-annual cycles were taken into account in order to quantify their
impact on seasonal and annual variability.
This paper is organized as follows: Sect. 2 describes data and
instrumentation used in this investigation and Sect. 3 describes the
Trend-Run model and presents the method adopted for data
analysis. Results detailing ozone trends and variability are presented
in Sect. 4. Section 5 concludes this investigation with a summary of
important results.
Ozone data source
TCO from ground-based observations
The ground-based instruments employed in this investigation are Dobson and
SAOZ spectrometers. TCO data obtained from these instruments are available
from the World Ozone Ultraviolet radiation Data Centre (WOUDC,
http://www.woudc.org/) and the Network for the Detection of Atmospheric
Composition Change (NDACC, http://www.ndsc.ncep.noaa.gov/) websites.
The Dobson spectrophotometer was the first instrument developed to measure
TCO (Dobson, 1931) and the first TCO measurement was made in 1926 at Arosa,
Switzerland. Dobson spectrophotometers were initially distributed at northern
middle latitudes and then later moved to include observations in the Southern
Hemisphere. At present, ground-based ozone measurements using Dobson
spectrophotometers are widespread globally. The Dobson network currently
consists of more than 80 stations, with most instruments calibrated following
the standard reference D 83 which is recognized by the ESRL (Earth System
Research Laboratory) and the WMO (Komhyr et al., 1993). The relative
uncertainty associated with the Dobson instrument has been assessed at
approximately 2 % (Basher, 1985). In this work, TCO recorded from Dobson
instruments located at Natal (equatorial site: 35.38∘ W,
5.42∘ S) and Irene (subtropical site: 28.22∘ E,
25.90∘ S) was used. The measurement principle of the Dobson
spectrometer is based on a UV radiation differential absorption technique in
a wavelength range where ozone is strongly absorbed compared to one where
ozone is weakly absorbed. Total column ozone observations are performed by
measuring the relative intensities at selected pairs of ultraviolet
wavelengths. The most used wavelengths are the double pair
(305.5/325.5 nm and 317.6/339.8 nm) and
(311.45/332.4 nm and 316.6/339.8 nm) emanating from the Sun,
moon or zenith sky (WMO, 2003, 2008). For further information regarding the
Dobson spectrometer functioning, the reader may refer to the studies outlined
by Komhyr et al. (1989, 1993).
The SAOZ spectrometer was developed by the CNRS (Centre National de la
Recherche Scientifique). In 1988, it was used for the first time at Dumont
d'Urville (Antarctica) to measure stratospheric ozone during the polar
winter. SAOZ is a passive remote sensing instrument operating in the visible
and ultraviolet ranges. It measures the sunlight scattered from the zenith
sky in the wavelength range between 300 and 600 nm. The SAOZ
instrument is able to retrieve the total column of ozone and NO2 in
the visible band with an average spectral resolution of around 1 nm
using the differential optical absorption spectroscopy (DOAS) technique. At
Reunion, a SAOZ instrument has been operational since 1993 within the
framework of NDACC. SAOZ ozone measurements have been performed during
sunrise and sunset with a precision of <5 % and an accuracy of <6 % (Hendrick et al., 2011). In the present work, the daily average of
TCO was taken as the mean value of sunrise and sunset observations
(86–91∘ SZA) recorded at Reunion. SAOZ TCO measurements have been
shown to be in good agreement with OMI-TOMS (Pastel et al., 2014; Toihir
et al., 2013) and Infrared Atmospheric Sounding Interferometer (IASI; Toihir
et al., 2015a) measurements. Further details can be found in Pazmiño
(2010).
TCO from satellite observations
Satellite data employed in this investigation were obtained from the EP-TOMS
and OMI-TOMS L2 data products. The EP-TOMS data product is the most recent
(1996–2005) and the duration of other important satellite observations
include the TOMS instrument onboard the Nimbus-7 satellite (1978–1993),
Meteor-3 (1991–1994) and ADEOS (1996–1997). On 2 July 1996, EP-TOMS was the
only instrument launched onboard the Earth Probe satellite. It was launched
into a polar orbit with an initial altitude of 500 km and an
inclination angle of 98∘. However, after the failure of the ADEOS
satellite, the EP-TOMS instrument was raised to an altitude of 739 km
with an inclination angle of 98.4∘ This was done in order to provide
complete global coverage of ozone and other species such as sulfur dioxide.
The TOMS instrument is a downward nadir viewing spectrometer that measures
both incoming solar energy and backscatter ultraviolet radiance at six
different wavelengths (379.95, 359.88, 339.66, 331.06, 317.35 and
312.34 nm) with a spatial resolution of 50km×50km. The instrument uses a single monochromator and a scanning
mirror to sample the backscattered solar ultraviolet radiation at 35 sample
points at 3∘ intervals along a line perpendicular to the orbital
plane (Bramstedt et al., 2003). TOMS data used in this work are an overpass
product and are available online from the website link
http://acdisc.gsfc.nasa.gov/opendap/EarthProbe_TOMS_Level3/contents.html.
For more details on the TOMS instrument and the associated data product, the
reader may refer to the EP-TOMS data product user guide document (McPeters
et al., 1998).
As previously mentioned, the EP-TOMS instrument was operational from
July 1996 to December 2005. TCO from TOMS was therefore combined with that
from OMI recorded overpass for the eight selected investigation sites in
order to produce a complete ozone dataset running to December 2012. Two OMI
L2 data products are available: OMI-DOAS and OMI-TOMS. However, the OMI-TOMS
L2 product was chosen for this investigation due to its good agreement with
the EP-TOMS data product (Toihir et al., 2014). OMI is a compact nadir
viewing instrument launched aboard the Aura satellite in July 2004 into
a near-polar helio-synchronous orbit at approximately 705 km in
altitude. OMI operates at a spectral resolution within 0.5 nm in
three spectral regions referred to as UV-1, UV-2 and VIS. In terms of spatial
coverage, its viewing angle is 57∘ under a swath width of
2600 km. The ground pixel size of each scan is
13×24 km2 in the UV-2 (310–365 nm) and visible
(350–500 nm) channels, and 13km×48km for
the UV-1 (270–310 nm) channel. OMI data used in this work are
overpass L2 products and are accessible from
http://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/OMI/V03/L2OVP/.
OMI-TOMS ozone data are retrieved based on two wavelengths (317.5 and
331.2 nm are applicable under most conditions, while 331.2 and
360 nm are used for conditions of high ozone concentration and high
solar zenith angle). The L2 product has a precision of ∼3 % and has
shown good agreement with Dobson and SAOZ measurements over the southern
tropics and subtropics (Toihir et al., 2013, 2015a). Further details relating
to the OMI instrument and mode of operation can be found in the OMI Algorithm
Theoretical Basis Document Volume II (Bhartia, 2002).
Ozone profiles from the SHADOZ network
Balloon-borne electrochemical concentration cell (ECC) ozonesonde devices
were used to provide profile ozone measurements over the eight stations
(Nairobi, Ascension Island, Java, Samoa, Fiji, Natal, Reunion and Irene)
investigated in this work. These stations form part of the SHADOZ network.
The geographical location of each site is illustrated in Fig. 1, while
geographical coordinates and altitude a.s.l. are given in Table 1. Equipped
with radiosondes for temperature, humidity and pressure measurements, the ECC
ozonesonde provides vertical profiles with a resolution between 50 and
100 m from ground to the altitude where balloon burst occurs (∼26–32 km). The precisions of SHADOZ measurements have been
evaluated to be ∼5 % (Thompson et al., 2003b). Further details on
ECC ozonesonde instrument validation, operating mode and algorithm used for
ozone partial pressure (in mPa) retrieval in each of the SHADOZ stations can
be obtained from Thompson et al. (2003a, 2007) and Smit et al. (2007). In
this work, analysis of vertical ozone variability was performed using SHADOZ
profiles recorded over 15 years (1998–2012). These data are available from
http://croc.gsfc.nasa.gov/shadoz/. The frequency of SHADOZ observations
is between one and six balloon launches per month. The monthly mean profiles
for a given station were taken as the average of recorded profiles during the
month for that station.
Geolocation of the SHADOZ sites selected for this
investigation.
Geolocation and mean sea level (MSL) of stations investigated in
this study.
Station
Region
Latitude
Longitude
MSL
(m)
Nairobi
Equatorial
1.27∘ S
36.8∘ E
1795
Natal
5.42∘ S
35.38∘ W
42
Java
7.57∘ S
112.65∘ E
50
As. Island
7.98∘ S
14.42∘ W
91
Samoa
Tropical
14.13∘ S
170.56∘ W
77
Fiji
18.13∘ S
178.40∘ E
6
La Reunion
Subtropical
21.06∘ S
55.48∘ E
24
Irene
25.90∘ S
28.22∘ E
1524
Data analysis
Methodology
Prior to analysis of TCO variability and trends, preliminary work was
completed in order to create a reliable ozone dataset for each station
by merging different available measurements. The first combination
exercise was performed on satellite data (TCO form OMI and TOMS) by
using the monthly values of ozone recorded during the overlap
observation period of the two satellites
(October 2004–December 2005). The relative difference (RD) between
TOMS and OMI with respect to OMI observation was calculated for
individual sites as follows:
RDm=100×TOMSm-OMImOMIm,
where “m” is the month in which both OMI and TOMS observations were
performed. After assessing the relative difference, the bias and root mean
square (rms) associated with the
difference were calculated. For further details outlining the method used to
calculate bias and rms, see Toihir et al. (2015a), Toihir et al. (2015b) and
Anton et al. (2011). The results obtained for each individual site are
presented in Table 2. The recorded biases between TOMS and OMI are positive,
thereby indicating an overestimation of TOMS total column ozone with respect
to OMI. However, the bias is less than 2.5 % and correlation coefficients
(C) between OMI and TOMS are higher than 0.9 (see column 4 of Table 2); it
is therefore reasonable to combine the two observations. As OMI measurements
recorded during the study period for the chosen sites show better agreement
with ground-based (Dobson and SAOZ) measurements than TOMS (see Toihir, 2016,
chapter 2), the combination of OMI and TOMS was performed using the OMI
observations as a reference. The data combination follows two steps: the
first step consists of adjusting the TOMS dataset (January 1998 to
December 2005) with respect to OMI by using the obtained rms. The absolute
rms is considered the mean systematic error in Dobson units between TOMS and
OMI. As TOMS ozone values are always higher than OMI values, TOMS time series
(1996–2005) can be adjusted to OMI measurements as follows:
TOMSadjusted(m)=TOMS(m)+rms.
The second step is to average the measurements from OMI with the
adjusted TOMS measurements recorded during the overlap observation
period of the two satellites.
The computed bias, rms (root mean square) and correlation
coefficient C observed between OMI and TOMS for individual stations during
the period from August 2004 to December 2006.
Stations
% bias (1σ)
rms (DU)
C
Nairobi
2.45 (1.09)
6.76 (2.85 %)
0.91
Natal
1.13 (0.82)
3.08 (1.19 %)
0.97
Java
1.12 (0.80)
3.01 (1.20 %)
0.98
Ascension
1.60 (0.57)
4.23 (1.60 %)
0.98
Samoa
1.36 (0.62)
3.39 (1.36 %)
0.96
Fiji
1.55 (1.16)
3.90 (1.48 %)
0.97
Reunion
1.60 (1.18)
4.46 (1.71 %)
0.98
Irene
2.42 (1.34)
6.44 (2.43 %)
0.97
Figure 2 presents the time evolution of monthly mean TCO measurements over
Reunion. Blue and black curves represent TOMS and OMI data respectively.
Figure 2 highlights the existing good agreement between both TOMS and OMI
observations, with similar results being reported in Toihir et al. (2014).
The merged satellite time-evolution data are shown as a dotted red line in
Fig. 2. The combination of the above satellite data is performed for each
site under study by using the rms obtained for the site (see Table 2).
Monthly mean of TCO as measured by TOMS (blue) and OMI
(black) over Reunion. The combined measurements of OMI and TOMS data
are indicated by the dashed red line.
A second method to validate this data combination consisted of a comparison
of satellite data with available ground-based measurements. TCO from
ground-based instruments were available at Natal, Reunion and Irene stations
and ground-based and TOMS adjusted-OMI time evolutions of the TCO monthly
mean over these stations are shown in Fig. 3. Figure 3 illustrates the fact
that satellite data show good agreement with ground-based observation before
and after the simultaneous period of TOMS and OMI observations, thereby
indicating a good agreement between the merged satellite and ground-based
data. Correlation coefficients C between the merged satellite measurements
and ground-based data are 0.88, 0.90 and 0.97 over Natal, Irene and Reunion
respectively. The obtained relative bias between satellite and ground-based
observation with respect to the ground-based observation is assessed to be
less than 2.5 % for individual sites. Due to this consistency existing
between merged satellite data and ground-based measurements, the study of TCO
variability and trends was performed using the merged satellite data for
sites where no ground-based measurements existed. In the case of three
stations where ground-based measurements were available, satellite and
ground-based measurements were merged by adjusting the satellite dataset with
respect to ground-based data.
Temporal evolution of TCO from ground-based spectrometers
(blue) compared with that obtained by a combination of TOMS and OMI
TCO overpass over Natal (a), Reunion (b) and
Irene (c) for the period January 1998 to December 2012.
The study of vertical ozone variability and associated trends was performed
based on SHADOZ ozone profile data available for the eight selected sites. In
this work, 3431 profiles were examined. The number of examined profiles and
the temporal coverage of data recorded for individual stations are presented
in Table 3. Although the eight SHADOZ stations started measurements in 1998,
the temporal coverage of the data recorded and the number of observations at
each station differ. For example, observations at Ascension Island were
discontinued in 2010, while no data were available in Natal during 2012.
Irene SHADOZ observations were discontinued in 2006 and restarted in
November 2012. Considering the discontinuity in observations and the limited
number of monthly profiles, monthly data for stations with the same
climatological behaviour were averaged
(Mzé et al., 2010). Stations were classified into three groups based on
low variability in zonal ozone distribution, the proximity of stations and
the dynamical structure of the stratosphere (Ziemke et al., 2010). These
groupings were near-equatorial (Nairobi, Natal, Java and Ascension Island),
tropical (Samoa and Fiji) and subtropical (Reunion and Irene) and
corresponded to stations located between latitude bands 0–10∘ S,
10–20∘ S and 20–30∘ S respectively. Geographical
coordinates of stations are given in Table 1. Monthly profiles recorded from
stations located in the same altitude band were averaged to represent the
monthly mean for the selected altitude range. Figure 4 shows the time and
height evolution of the constructed monthly mean ozone profile. The top (a),
middle (b) and bottom (c) panels represent the monthly vertical distribution
of ozone concentration between 15 and 30 km over the equatorial,
tropical and subtropical regions respectively. In order to follow a more
uniform data processing approach, monthly TCO values for stations from the
same latitude range were averaged for specific cases. The constructed TCO
time series from the procedure defined above are shown by the blue line in
Fig. 5.
Time–height section of ozone concentration
(molcm-3) obtained by mean monthly profiles recorded
over stations located in the equatorial (a),
tropical (b) and subtropical (c) regions from
January 1998 to December 2012.
Time period and total number of height profiles used in this
analysis for selected stations.
Station
Time period
Number
of profiles
Nairobi
[1998–2012]
627
Natal
[1998–2011]
478
Java
[1998–2012]
323
As. Island
[1988–2010]
459
Samoa
[1998–2012]
518
Fiji
[1998–2012]
319
La Reunion
[1998–2012]
461
Irene
[1998–2007, 2012]
246
Time evolution of monthly mean total ozone values (blue) over
the equatorial (a), tropical (b) and
(c) subtropical regions. The black lines represent TCO
values as calculated by the Trend-Run model.
Description of the Trend-Run model
Trend-Run is a multi-regression model adapted by Reunion University and
dedicated to the study of ozone and temperature variability and associated
trends (Bencherif et al., 2006; Bègue et al., 2010; Toihir et al., 2014).
Input parameters include monthly mean ozone values (in DU for TCO or
concentration at a given pressure for high-altitude profiles) and variables
that represent a significant contribution to stratospheric ozone variability.
Considering the selected region (0–30∘ S) and the temporal coverage
defined in this work (1998–2012), and in order to simplify analysis and
interpretation of results, the parameters included in this analysis were QBO,
ENSO and solar flux. Note that aerosols constitute a source of uncertainty
that may affect TCO variability, notably following a major event such as
a volcanic eruption. However, by using the Trend-Run model, Bencherif
et al. (2006) and Bègue et al. (2010) showed that volcanic aerosol
forcing from the Pinatubo eruption was weak and could be assumed negligible
beyond 1996. Aerosol index is therefore not included in the framework of this
present study as the ozone time series starts in 1998, 2–3 years after the
post-Pinatubo eruption period. In trend calculations, a long-term linear
function is generated by the model to characterize the trend index which is
used among the model parameters. The trend value is calculated based on the
slope of the normalized linear function. As ozone variability is also
affected by annual and semi-annual oscillations (AO and SAO), these two
parameters were taken into account in terms of model inputs. They may be
described by a sinusoidal function as shown below:
AO(m)=cos2πm12+φ,SAO(m)=sin2πm6+φ,
where “m” is the monthly temporal parameter and φ=2πI180.
I is a phasing coefficient between the temporal signal of ozone and the
sinusoidal annual or semi-annual function. The ENSO parameter is based on the
Multivariate ENSO Index (MEI), while the solar cycle is the solar
10.7 cm radio flux. These two parameters were obtained from the
NCEP/NCAR website:
http://www.esrl.noaa.gov/psd/data/climateindices/list/. The MEI is
defined by positive values during El Niño and negative values for the
duration of La Niña. The chosen QBO index is the time series of zonal
wind at 30 hPa over Singapore and is available from the following
link: http://www.cpc.ncep.noaa.gov/data/indices/qbo.u30.index. ENSO and
QBO parameters are phased with respect to ozone time series based on phasing
time which define the temporal point where the variable response to ozone is
maximum. The index time series of the considered parameters are first
normalized and filtered to remove a possible 3-month response. The output
geophysical signal Y(z,m) of ozone for a given altitude “z” is
modelled as follows:
Y(z,m)=C(z)1+C(z)2AO(m)+C(z)3SAO(m)+C(z)4SC(m)+C(z)5QBO(m)+C(z)6MEI(m)+C(z)7TREND(m)+ε(z,m),
where C(z)(1–7) are regression coefficients representing the
weighting of parameterized variables on geophysical signal Y and
ε(z,m) is the residual term representing a noise and/or
contribution of parameters not included in the model. The regression
coefficients are determined based on the least-squares method in order to
minimize the sum of the residual squares. It is worth noting that the degree
of data independency is assessed through the autocorrelation coefficient
φ of the residual term (Bencherif et al., 2006). More details on how
φ is calculated for ozone data are found in Portafaix (2003). The
uncertainties in coefficient C(z)(1–7) are assessed by taking
into account the autocorrelation coefficient and are formulated as follows:
σa2=v(k)⋅σs2⋅1+ϕ1-ϕ,
where σs2 and v(k) represent the variance of the residual term
and the covariance matrix of the different proxies input in the linear
multi-regression model respectively. Equation (5) is used to estimate error
associated with trend estimation.
The temporal function C(z)p+1⋅Yp(m1→n) is defined as the
factorized signal of input proxy “P”, and C(z)p+1⋅σYp(m1→n) is the corresponding SD (Brunner et al., 2006). The
contribution of a given parameter to the total variation of inter-annual data
is the ratio of a factorized signal sum of squares to the total sum of
squares of the inter-annual ozone data. The amplitude of the response of the
parameterized parameter (in percentage by unit of the parameter) with respect
to the total variance of ozone time series is assessed based on the
corresponding regression coefficient. It may be expressed as follows:
A(z)P=100×C(z)P+1C(z)1.
A is positive when the temporal signal of the parameter is in phase with
the ozone time series, and it is negative in the opposite case.
Results and discussion
In this investigation, ozone inter-annual variability and trends were studied
using the Trend-Run model as described above. The contribution and response
of dynamical forcings to TCO variability are presented and discussed in
Sect. 4.2. The aim of this study was to analyse the behaviour of each
individual forcing contribution and to quantify the effect of its
contribution on ozone variability in terms of TCO and mixing ratio profiles
for the 15–30 km altitude range. The observed solar flux response to
ozone over the selected altitude range is very weak (less than 1 %); the
solar flux contribution to ozone vertical distribution is therefore not
discussed. Ozone trends (for the observation period) as estimated by the
model are presented (per site) in the final subsection.
Model assessment
In order to quantify the fit of the regression model with original ozone
data, a statistical coefficient R2 was used. The coefficient R2 is
defined as the ratio of a regression sum of squares to the total sum of
squares (Bègue et al., 2010) and it measures the proportion of the total
variation in ozone as described by the model. When the regression model
accounts for most of the observed variation in ozone time series, the value
of R2 is close to unity. For the cases where the model shows little
agreement with measured data, R2 decreases toward zero (Bègue
et al., 2010; Toihir et al., 2014). Figure 5 shows the time evolution of
total column ozone as simulated by the multi-regression model (black). The
blue lines correspond to TCO measurements performed over the (a) equatorial,
(b) tropical and (c) subtropical latitude bands. A good agreement between
model and observations is apparent over the three regions and the best fit is
observed for the extratropics. This is because the variability is usually
influenced by the seasonal cycle which is well accounted for by the
regression model (Eq. 3). The values obtained for the statistical coefficient
R2 were 0.75, 0.71 and 0.91 for the equatorial, tropical and subtropical
bands respectively. These results indicate that the model describes
approximately 75, 71 and 91 % of TCO variability. This implies that the
sum of the contributions of the annual cycle, semi-annual cycle, QBO, ENSO,
solar flux and trend to TCO variability reached 75, 71 and 91 % for the
equatorial, tropical and subtropical bands respectively. That represents how
the model is able to reproduce most of the variability of the studied ozone
time series. Statistical coefficients (R2) higher than 0.82 were
highlighted by Toihir et al. (2014) over sites located at 30–40∘ S,
while Bencherif et al. (2006) and Bègue et al. (2010) demonstrated that
the Trend-Run model is able to describe approximately 80 % of the
temperature variability observed over Durban (South Africa) and Reunion.
Regarding vertical ozone distribution, profiles plotted in Fig. 4 were
interpolated at a vertical resolution of 0.1 km which resulted in 150
altitude levels for the range between 15 and 30 km. The
multi-regression model calculation was performed for each altitude level and
profiles of the coefficient of determination R2 obtained at
(a) equatorial, (b) tropical and (c) subtropical bands are shown in Fig. 6.
The calculated R2 values were higher than 0.6, and this indicates that
the input parameters account for more than 60 % of ozone variability in
the 15–30 km altitude range.
Vertical profiles of R2 (determination coefficient)
calculated by the Trend-Run model for the selected latitude range:
(a) equatorial, (b) tropical and
(c) subtropical regions.
A study by Brunner et al. (2006) demonstrated that high values of R2
characterized a regime dominated by seasonal variability, while R2
values between 0.3 and 0.7 described cases where ozone variability was
dominated by QBO. This is in agreement with results from the present study
and confirms the capacity of the Trend-Run model to retrieve consistent ozone
time series. As seen from Fig. 6, values of R2 greater than 0.8 were
recorded at different altitude layers depending on the region, namely, in the
16.3–26.0 km range for the equatorial region, in the
17.3–22.2 km layer for the tropics and between 18.9 and
24.5 km for the extratropics. Furthermore, this analysis demonstrates
that the noted altitude ranges are dominated by a seasonal variability which
is expressed by an annual cycle function in the model.
TCO variability
Seasonal variability
Figure 7 illustrates ozone climatology obtained by compilation of 15 years of
data recorded between January 1998 and December 2012. Three regions are
explored: (a) equatorial, (b) tropical and (c) subtropical. Over these
selected regions, total ozone exhibited high values from July to November.
A positive gradient was observed from May to the maximum annual peak in
September (equatorial region) but occurred a month later in October for
tropical and near-subtropical regions. The recorded positive gradient over
the subtropics was the largest and this indicated a high seasonal variability
in this region compared to tropical and equatorial zones. Low ozone levels
were recorded during the austral summer/autumn period over the three regions.
Ozone recorded in the equatorial region was high in March, April and May
compared to the tropics. Two peaks were clearly observed for the equatorial
region, and these highlight a semi-annual cycle with a maximum in
September–October and a second maximum in March–May.
Seasonal variations of total ozone over three different
regions: equatorial (a), tropical (b) and
subtropical (c) regions. Error bars represent
±1σ.
These results can be explained as follows: stratospheric ozone is formed
through a photochemical reaction (Chapman, 1930) that requires a sufficient
quantity of solar radiation. The equatorial region is therefore the primary
site of ozone production (Coe and Webb, 2003). During the period close to
equinox, solar radiation leads to an increase in ozone production over the
equatorial region. During summer, ozone transport consists primarily of
vertical upwelling movements which are confined to the low-latitude region.
This results in less ozone being distributed to the subtropics. This effect
is the main contributing factor to why a total ozone peak is observed in
autumn over the equatorial region (Mzé et al., 2010). However, the
semi-annual equinox process may not be the sole explanation for the maximum
value recorded in the equatorial region during September spring equinox,
because it is generalized over the three regions. High ozone levels recorded
in winter/spring are due to transport and accumulation of ozone in the
Southern Hemisphere as a result of Brewer–Dobson circulation on a regional
(air mass transport from the tropics to subtropics) and global scale (from
summer hemisphere to winter hemisphere) (Holton et al., 1995; Fioletov,
2008). Transport of ozone from the tropical to extratropical regions is the
most dominant process during the winter period (Portafaix, 2003) and
constitutes the principal reason for the observed annual maximum over the
region 20–30∘ S. The decline of ozone levels during late spring and
summer is explained by a decrease in ozone transport coupled with ozone
photochemical loss (Fioletov, 2008). However, the observation of maximum
ozone in September over the equatorial region but 1 month later over the
subtropics may be explained by a delay of ozone transport between the two
regions. As shown in Fig. 7, the curve of the seasonal distribution of TCO
attributed to (b) the tropical region is below that of (a) the equatorial
zone from January to September. While considering the process of formation
and transport of ozone as described above, TCO annual records in tropical
regions should be higher than that recorded over equatorial regions. However,
it is important to note that both sites (Samoa and Fiji, in the
10–20∘ latitude range) are located in the Pacific Ocean. The low TCO
quantity observed in the Pacific is due to a low tropospheric ozone levels
over the region as a result of a zonal Wave-One observed on tropical
tropospheric ozone (Thompson et al., 2003a and 2003b). Similar results were
reported by Thompson et al. ( 2003a and 2003b) where low ozone levels over
SHADOZ Pacific sites were observed with respect to African and Atlantic sites
due to the zonal Wave-One.
The contribution of seasonal cycles (annual and semi-annual oscillations) to
ozone variability obtained from the regression analysis is presented in the
first and second columns of Table 4. The calculated values show that except
for Nairobi, the annual cycle constitutes the predominant mode of ozone
variability over the studied sites and its influence is strongly observed
over the subtropics. The contribution of annual oscillations to ozone
variability exhibits a latitudinal signature, with its minimum and maximum at
equatorial and subtropical zones respectively. The percentage contribution of
annual oscillations to TCO variability decreases equatorward from Irene
(65.96±3.93 %) to Nairobi (20.33±1.89 %). As explained in
Sect. , the annual cycle is modeled by a sinusoidal function with
maximum and minimum in winter/spring and autumn/summer. This kind of annual
variation mode characterizes the ozone climatology of southern tropics and
subtropics (Toihir et al., 2013, 2015a). The response of annual oscillations
to ozone variability obtained for individual sites and reported in Table 5 is
positive. This indicates that the modeled annual cycle function is in phase
with the original ozone data. However, the amplitude of the annual cycle on
total variance of ozone over the subtropics is the highest, and this
indicates that ozone variance is sensitive to annual oscillation in the
subtropics compared to tropics. The amplitude was evaluated at approximately
5.6 % by unit of the standardized annual function in subtropical sites
(Reunion and Irene) and varied between 1 and 3 % from Fiji towards the
Equator.
Percentage of the contribution and corresponding SD of
the annual cycle, semi-annual cycle, 11-year solar cycle, QBO and ENSO to
total
ozone variability for individual sites, as obtained by the Trend-Run
regression model. The last column shows the corresponding value for the
coefficient of determination R2.
Station
AO (%)
SAO (%)
Solar flux (%)
QBO (%)
ENSO (%)
R2
Nairobi
20.33±1.89
5.29±1.02
5.91±0.83
20.48±1.92
5.67±0.87
0.70
Natal
32.60±2.26
12.18±1.34
5.61±0.97
9.33±1.22
5.78±0.64
0.76
Java
32.61±1.37
13.88±1.16
7.70±0.86
8.68±0.75
10.75±0.80
0.76
As. Island
34. 15±2.04
15.51±1.29
7.81±0.43
8.23±0.82
7.27±0.54
0.75
Samoa
36.15±1.78
9.05±0.83
7.06±0.41
5.56±0.67
11.63±0.99
0.72
Fiji
37.55±2.13
6.79±1.34
5.13±0.95
5.22±0.96
7.91±0.28
0.71
Reunion
64.66±3.94
6.33±1.55
4.38±1.12
3.66±0.89
0.07±0.03
0.90
Irene
65.96±3.93
6.26±1.24
4.65±1.19
3.60±1.07
0.001±0.02
0.87
Response values of the chosen proxies (annual cycle, semi-annual
cycle, solar flux, QBO and ENSO) to total ozone variability for individual
sites as obtained by the Trend-Run model. The response is given in percent
by unit of the normalized proxy.
Station
AO
SAO
Solar flux
QBO
ENSO
Nairobi
1.66
1.52
1.60
3.04
2.15
Natal
1.96
1.73
1.59
2.95
-2.55
Java
1.87
1.90
1.88
2.55
2.87
As. Island
2.83
1.96
1.90
2.34
-3.08
Samoa
2.44
1.42
1.50
2.42
-3.47
Fiji
3.03
1.40
1.65
-1.44
-3.16
Reunion
5.56
1.27
1.26
-2.33
0.15
Irene
5.56
1.27
1.18
-2.46
0.04
Results presented in Table 4 show that on average, 13.86±1.26 % of
ozone variability can be explained by semi-annual oscillations over stations
located at 5–10∘ S. However, the semi-annual pattern was not
apparent over Nairobi compared to the rest of the equatorial sites. The
predominant mode of ozone variability in this equatorial site was the QBO
(20.48±1.92 %), followed by the annual oscillations (20.33±1.89 %). The semi-annual oscillation pattern of TCO was observed to be
significant at low latitude, with its maximum contribution over Ascension
Island. The response values associated with semi-annual oscillation to ozone
variability are presented in column 2 of Table 5 and show that a maximum in
amplitude over Ascension Island was observed. Both quantities (contribution
and response) decreased gradually while moving away from the Ascension Island
site towards the Equator or southward. This indicates that the semi-annual
oscillations of ozone are weighted at approximately 8∘ of latitude
and coincide with the location where the influence of equinox processes on
ozone variability is the greatest.
QBO contribution
The QBO constitutes the second most important parameter influencing ozone
variability after seasonal oscillations. The QBO behaviour on ozone time
series can be observed by removing the monthly climatological value from its
monthly mean. In this investigation, the deseasonalized ozone signal was
further subjected to 3-month smoothing in order to filter out smaller
perturbations from intra-seasonal oscillations and to address the component
due to ozone biennial oscillations.
Figure 8 illustrates the deseasonalized ozone (blue dashed curve) time series
(1998–2012) for equatorial and subtropical regions. These data are
superimposed with the monthly mean zonal wind data at 30 hPa over
Singapore (black curve). From Fig. 8 it can be seen that the predominant
variability of deseasonalized monthly ozone data is an approximately 2-year
cycle linked to the QBO and represented by the zonal wind data at
30 hPa. However, the equatorial and subtropical ozone signals seem to
be opposite in phase. This indicates that if an excess of ozone is recorded
over the equatorial region, the subtropical zone shows a trend illustrating
a deficit with respect to the monthly climatological value and vice versa.
The QBO change from the Equator to the subtropics is due to a secondary
circulation having an upwelling branch in the subtropics and a subsidence
branch in the tropics during the westerly phase of the QBO (Chehade
et al., 2014). This secondary circulation is characterized by a deceleration
or an acceleration of the Brewer–Dobson circulation (BDC) during the
westerly or easterly phase of the QBO respectively. The deceleration of the
BDC leads to an increase in ozone in the tropics, while the acceleration
leads to a decrease in ozone in the tropics. Due to this circulation, the
deseasonalized TCO data over the equatorial region are in phase with the QBO
index. Positive ozone anomalies correspond to the QBO westerly phase and
negative anomalies are linked to the QBO easterly phase. Similar results have
been determined in previous studies (Randel and Wu, 2007; Butchart
et al., 2003; Zou et al., 2000; Bourassa et al., 2014; Peres et al., 2017).
Deseasonalized time series of total ozone (blue line) for
equatorial (0–10∘ S) and subtropical (20–30∘ S)
regions calculated by subtracting the ozone monthly mean from the
corresponding monthly climatological value. The black line is the
time series in months of zonal wind recorded at 30 hPa over
Singapore.
The above process was explained by Peres et al. (2017) and may be outlined as
follows: the descending westerly phases of the QBO are associated with
a vertical circulation characterized by downward motion in the tropics and
upward motion in the subtropics. This leads to a weakening of the normal
speed of the Brewer–Dobson circulation. In this way the upward motion of air
mass is slowed down and the tropopause height decreases. As the ozone mixing
ratio increases with the increase in altitude in the lower stratosphere,
ozone production can occur for a longer period than normal (Cordero
et al., 2012). This mechanism leads to a positive column ozone anomaly at low
latitudes and a negative anomaly in the subtropics. During the QBO descending
easterly phase, ozone formed spends less time at low latitude due to the
enhanced Brewer–Dobson circulation (Cordero et al., 2012). The newly created
ozone is rapidly transported to the subtropics, resulting in a negative ozone
anomaly at lower latitudes and a positive ozone anomaly in the extratropics.
Butchart et al. (2003) explained that the equatorial ozone anomalies due to
QBO forcing extend to 15–20∘ of latitude. In order to investigate
the border between equatorial and subtropical QBO signals on ozone, the
response of the QBO signal to TCO variability for individual sites was
calculated based on the regression coefficient associated with the QBO on the
Trend-Run model (Eq. 4). The response values are given in column 5 of
Table 5. It is seen that the response of QBO to TCO variability by unit of
normalized QBO signal is positive when the QBO is in phase with the ozone
signal and negative for the reverse case. In this study a positive response
(of decreasing magnitude) is obtained with the decrease in latitude moving
from Samoa (14.13∘ S) to Nairobi (1.27∘ S). Conversely the
responses obtained are negative at Fiji (18.13∘ S), Reunion
(21.06∘ S) and Irene (25.90∘ S). These results indicate
that the border between the equatorial and subtropical QBO-ozone signature is
found at around 15∘ in latitude. Similar results were reported by
Chehade et al. (2014) by calculating the coefficients of regression
associated with the influence of the QBO on ozone variability. The Chehade
et al. (2014) study reported positive and negative regression coefficients
between 0∘ and 15∘ S and from 15∘ S towards the
pole respectively.
In this work, positive ozone anomalies with amplitude varying between 7.5 and
13.9 DU were recorded over the equatorial sites during the zonal wind
westerly phase, while the negative anomalies observed during the easterly
phase oscillated between -13.3 and -2.80 DU. These results indicate that
inter-annual oscillations of TCO over the equatorial regions have maximum
amplitude varying within ±14 DU during the study period (1998–2012).
This is in agreement with the results of Butchart et al. (2003). For 1980 to
2000 they obtained a deseasonalized ozone signal which varied within
±14 DU over the Equator.
It is worth noting that the maximum amplitude of ozone anomalies observed in
the subtropics is lower (within ±11 DU) than the equatorial region
(±14 DU). These amplitude values indicate that the QBO modulation on
inter-annual variability of ozone is higher over the equatorial region in
comparison to the extratropics. Values reported in Table 4 show a clearly
decreasing QBO contribution poleward, i.e. from Nairobi (20.48±1.92%) in the equatorial zone to Irene (3.60±1.07%) in the
subtropics.
ENSO contribution
Column 6 of Table 4 presents the percentage of ENSO contributions to
inter-annual ozone variability for individual sites. It is clear that the
ENSO parameter does not exert much influence on ozone variability over the
subtropical sites (Reunion and Irene), where its contribution to TCO
variability is assessed to be less than 1 %. However, the ENSO
contribution is more pronounced over tropical and low-latitude sites located
between 19∘ S and the Equator. In this latitude band, the ENSO
contributions observed at Fiji, Samoa, Ascension Island and Java are high
compared to Natal and Nairobi. An average ENSO contribution of 9.40±0.65% to total ozone variability over Fiji, Samoa, Ascension Island and
Java is observed, while a corresponding value of 5.73±0.75% for
Natal and Nairobi is seen (see Table 4). These results indicate that the ENSO
influence on total ozone variability is high over the tropics compared to
that in the equatorial zone and subtropics. It has been reported by Randel
et al. (2009) that ENSO originates in the tropics and is linked to coupled
atmosphere–ocean dynamics. This factor may be the main reason for the
observed high ENSO contribution over sites located in the tropics compared to
low-latitude and subtropical sites. Zerefos et al. (1992) removed the QBO,
seasonal and solar cycles from the ozone signal at Samoa and obtained a good
correlation between ENSO and ozone signal. They mentioned a possible
ENSO–ozone relationship beyond the tropical region only during very large
ENSO events (e.g. 1982–1983, 1997). In this study, the low estimated ENSO
contribution (less than 1 %) for the subtropical region may also be
explained by the fact that there were few large ENSO events recorded during
the investigated period (1998–2012).
The tropical sites located in the Pacific Ocean (Samoa, Fiji and Java) were
more influenced by ENSO when compared to tropical and equatorial sites
(Ascension Island, Natal and Nairobi) due to the high ENSO activity usually
observed in the Pacific Ocean (Randel and Thompson, 2011; Chandra et al.,
1998, 2007; Logan et al., 2008; Ziemke and Chandra, 2003; Ziemke
et al., 2010). In addition, the Ascension Island ENSO contribution was higher
compared to those observed at Natal and Nairobi. As ENSO is a coupling
ocean–atmosphere event generated in the Pacific Ocean (Ziemke and Chandra,
2003; Rieder et al., 2013), the low ENSO contribution recorded over Natal and
Nairobi compared to Ascension Island can be explained by their continental
location. As Ascension Island is located in the Atlantic Ocean, the high ENSO
contribution observed at this site is probably due to a Pacific–Atlantic
Ocean connection.
The ENSO response to ozone variance is assessed and shown in the last column
of Table 5. The aim of this work is to define the mean behaviour of ozone
variance for individual sites during ENSO events. It is worth noting that the
mean ENSO influence is observed on tropospheric ozone (Randel and Thompson,
2011; Ziemke and Chandra, 2003). However, due to weak variability in
stratospheric ozone over the tropics (Ziemke et al., 1998) during periods of
normal conditions, tropospheric ozone changes were shown to affect the
stratospheric ozone and total column ozone variability over the tropics
during ENSO events (Rieder et al., 2013). As the MEI is characterized by
positive values during El Niño, a positive ENSO response indicates an
increase in total column ozone (TCO) and a negative response indicates
a decrease in TCO. The ENSO response to ozone variability over subtropical
sites (Reunion and Irene) is positive, while it is generally negative over
the tropics (except over Java and Nairobi). These results are in good
agreement with those reported by Chehade et al. (2014) which are obtained
using a regression analysis model. Through their study they obtained negative
(positive) ENSO regression coefficients indicating a negative (positive) ENSO
response to ozone variability over the tropics (extratropics). It has been
mentioned in several papers (Rieder et al., 2013; Randel et al. 2009; Randel
and Thompson, 2011; Frossard et al., 2013; Chehade et al., 2014) that
a negative ENSO response to ozone variability in the tropics is linked to
enhanced transport of an ozone-rich air mass from the tropics to the
extratropics due to strengthening of the Brewer–Dobson circulation in the
stratosphere during ENSO warm events. As reported by Rieder et al. (2013),
ENSO events are associated with more frequent stratospheric warming, an
increase in tropopause height and a decrease in stratospheric ozone in the
tropics. By comparison, in the subtropics, the tropopause height decreases
and stratospheric ozone increases. This effect could be the reason for the
observed ENSO positive response over Irene and Reunion (subtropics). The ENSO
warm events produce suppressed convective movements over the western Pacific,
leading to a positive anomaly of ozone in this region, while the air mass
convection is enhanced over the eastern Pacific that leads to a reduction of
ozone (Ziemke and Chandra, 2003). For the period 1970–2001, Ziemke and
Chandra (2003) obtained a positive El Niño response corresponding to an
average peak of positive ozone anomaly evaluated at 5 DU over the Indonesia
area. Furthermore, they found a negative ozone anomaly in the eastern
Pacific, the location of Fiji and Samoa. According to Ziemke and Chandra,
(2003), this could be the main reason for the observed positive ENSO response
at Java in the western Pacific and negative response over Samoa and Fiji in
the eastern Pacific. The connection between the western Pacific and the
Indian Ocean could be among the reasons for the observed positive ENSO
response to ozone variability at Nairobi in the eastern African and Reunion
in the western Indian Ocean region.
Solar flux contribution
Variation in total ozone concentration also depends on solar flux intensity
over the tropics and subtropics. Figure 9 illustrates the variation in annual
total ozone anomaly calculated between 1998 and 2012 over the three regions
(bottom panel) and the annual 10.7 cm solar radio flux variation
during the same period (top panel). Inspection of Fig. 9 illustrates the
effect of the 11-year solar cycle on inter-annual total ozone variations.
Negative ozone anomalies are observed during periods of low sunspot
intensity, thereby confirming the dependence of ozone production on incident
solar radiation flux. The converse is also true; that is, high annual total
ozone levels are associated with periods of high sunspot intensity. The
results presented here are in good agreement with Soukharev and Hood (2006).
The latter obtained negative (positive) monthly mean ozone anomalies during
periods with low (high) Mg II solar UV intensity. The dependence of annual
ozone levels on annual solar radiation is also highlighted by Efstathiou and
Varotsos (2013). In this investigation, the high levels of TCO were observed
over the tropics when solar activity was at a maximum (2001) and compared to
values recorded during a minimum in solar activity in 2008. This illustrated
the impact of the solar cycle on global ozone concentrations. In the work
presented here, considering the year with solar maximum (2001) to the year
with minimum solar intensity, a variation in ozone levels was observed
corresponding to approximately 1.05, 1.52, and 1.60 % for the
(a) equatorial, (b) tropical and (c) extratropical regions respectively. It
is expected that the observed percentage change in ozone levels is directly
related to solar flux activity. A study by Zerefos et al. (1997) confirmed
that the solar flux component in TCO between 1964 and 1994 was approximately
1–2 % over decadal timescales. It can therefore be concluded from the
results presented here, together with those of Zerefos et al. (1997), that
the solar flux contribution to ozone variability did not vary significantly
during the past 6 decades.
(a) Annual mean of 11-year solar flux recorded from
1998 to 2012 and (b) the annual TCO anomaly recorded for
the same period in equatorial, tropical and subtropical regions.
As the solar flux index is one of the input parameters in the regression
model, the contribution and response of the 11-year solar cycle to total
ozone variability were assessed. The effect of changes in solar flux on ozone
levels is presented in the fourth column of Table 5. The obtained responses
are always positive, indicating that the solar flux index is in phase with
total ozone time evolution as reported in the WMO (2014). The increase in
ozone occurs when the sunspot intensity increases and vice versa. The average
magnitude of the solar flux response to ozone variability is about 1.57 %
by unit of solar flux index. The contribution of solar flux to total ozone
variability over the study region varies by between approximately 4.38 and
7.81 %. The contribution of the solar cycle is high compared to the
3 % reported by the WMO (2010, 2014) on the global scale. This is
probably due to (1) the length of the data records (less than two solar
cycles), (2) the time period under investigation (1998–2012) and (3) the
studied region (from equatorial to around 40∘ S). However, the
contribution values obtained exhibit a minimum at Reunion in the subtropics
and a maximum at Ascension Island in the lower-latitude region. These results
indicate a high solar flux influence over the low-latitude region in
comparison with the subtropics. The contribution of solar flux to total ozone
variability decreases gradually by moving away from Ascension Island
equatorward or southward. This solar flux contribution pattern is similar to
that observed in semi-annual oscillations (see Sect. 4.2.1). From the results
presented in this study it can be inferred that semi-annual variations are
modulated by solar activity over the tropics and subtropics and that both
semi-annual and 11-year solar forcings control ozone distributions.
Height profile ozone variability
The main objective of this section is to provide additional details regarding
the contribution of proxies to ozone variability for different altitude
levels from 15 to 30 km. This exercise is based on vertical
distribution of ozone concentration as presented in Fig. 4. Here the ozone
profiles are interpolated with 0.1 km vertical resolution giving 150
altitude levels in the 15–30 km altitude range. The Trend-Run model
is applied to each altitude level.
Seasonal contribution to ozone profile variability
The contributions of annual and semi-annual oscillations to ozone variability
(15 to 30 km) are shown in Fig. 10. Figure 10 reveals that seasonal
cycles are the most dominant forcings to ozone variability in the lower
stratosphere. The contribution of annual oscillation (AO) to ozone
variability is highlighted over the three studied regions and accounts for
more than 40 % in the UT–LS (upper troposphere–lower stratosphere). AO
maximum amplitudes in equatorial and tropical regions are found around the
tropical tropopause layer (TTL) below 20 km and around
22.5 km in the subtropics. These results are in good agreement with
recent findings. Gebhardt et al. (2014) showed that in the lower stratosphere
(21 km), ozone variability is dominated by AO in the tropics
(20∘ N–20∘ S). Furthermore, Eckert et al. (2014) explained
that vertical motion in the TTL had a significant impact on ozone variance
and that seasonal variations contributed more than 50 % to ozone
variability in this layer. A strong annual cycle signature in the UT–LS has
also been reported by Bègue et al. (2010). The important contribution of
the annual cycle in the UT–LS may be due to the STE
(stratosphere–troposphere exchange) processes which occur seasonally in the
tropics. These exchanges are linked to the upwelling air mass through the
tropopause in summer and the submersion of stratospheric air mass to the
troposphere during winter. These air mass exchanges affect the seasonal
budget of ozone and temperature from equatorial to middle latitude. As seen
in Fig. 10, annual cycle contributions higher than 40 % are found from 15
to 20 km over the equatorial region. However, the annual cycle
contributions higher than 40 % observed over the tropical region are
found from 17.5 to 22 km. In addition, the maximum annual cycle
contribution for the equatorial region is observed at 18.5 km, whilst
in the tropics it occurs at 19.5 km. These observations support the
existence of upward propagation of the AOs of ozone below 25 km and
the amplitude of these AOs increases with the increase in latitude poleward.
This may explain the high annual cycle contribution observed in the
15–24.7 km altitude range in the subtropics. The presence of an
annual signature in ozone temporal evolution in this wide altitude band
(15–24.7 km) in the subtropics is inconsistent with the strong
annual variability observed in TCO, as discussed in the previous subsection.
Height profile in % of the contribution of the annual cycle
(a1–c1) and the semi-annual cycle (a2–c2) to ozone
variability as calculated by the Trend-Run model over the
equatorial (a1, a2), tropical (b1, b2) and
subtropical (c1, c2) regions. SDs associated with
contributions are presented in grey dotted lines.
It is worth noting that the seasonal cycles of ozone observed at altitudes
below 25 km are linked to dynamical processes, while above an
altitude of 27 km, seasonal cycles are modulated by ozone
photochemical processes. Contributions of the annual forcing of greater than
10 % are observed at altitudes above 27 km. Here the contribution
part of annual oscillation increases with altitude, especially over the
tropical and subtropical regions. These results support the existence of an
annual cycle signature on ozone temporal evolution over altitudes above
30 km in the tropical and subtropical regions. Eckert et al. (2014)
found that ozone variation due to AO was greater than 10 % above
27 km in the 10–30∘ S latitude band. However, this
amplitude decreases above 35 km, where ozone variability is strongly
controlled by semi-annual oscillation (SAO). Furthermore, the maximum
amplitude of the ozone SAO signal observed by Eckert et al. (2014) was
centered slightly above 30 km over the tropics. Mze et al. (2010)
observed a strong semi-annual cycle in equatorial ozone climatology between
27 and 36 km, with a maximum peak at 31 km, and Gebhardt
et al. (2014) observed ozone variability dominated by SAO in the middle
(35 km) and upper tropical stratosphere (44 km) in the
tropics (20∘ N–20∘ S).
The (SAO) contribution to ozone variability is highlighted in the present
work for altitudes higher than 27 km for the equatorial and tropical
regions. Above 27 km, the SAO contribution accounts for more than
10 % of total ozone variability over the equatorial region, while this
effect is reduced to less than 10 % for the tropics. This confirms the
high contribution of SAO to ozone variability at low latitudes with respect
to other regions as discussed in Sect. 4.1.1. As supported by Eckert
et al. (2014), beyond the tropics the SAO amplitude decreases gradually to
near zero. As reported in previous studies, the SAO is linked to change in
the zonal wind regime at the equatorial stratopause, where the maximum
component appears during solstice (when easterly) and equinox (when westerly)
(Belmont, 1975; Hirota, 1978; Delisi and Dunkerton, 1988). This mechanism
leads to temperature anomalies corresponding to 2–4 K in the upper
stratosphere (Nastrom and Belmont, 1975), thereby driving a change in the
rate of ozone production and loss (Maeda, 1984) during the solstice and
equinox periods. As demonstrated in Sect. 4.2.1, the equinox period is
characterized by maximum peaks of TCO production over the equatorial region,
while the solstice corresponds to minimum ozone levels (or ozone loss) over
equatorial regions.
QBO contribution to ozone profile variability
QBO contributions to ozone profile variability between 15 and 30 km
are presented in Fig. 11 for the three regions, namely (a1) equatorial,
(b1) tropical and (c1) subtropical. A strong contribution of the QBO to ozone
variability is observed over the 20–28 km altitude range for the
equatorial region. Here the QBO is the most dominant mode and accounts for
more than 30 % of ozone variability. These results are in agreement with
previous work in which a strong signature of the QBO on inter-annual
variability of ozone with large amplitudes over the tropics at altitudes of
approximately 20–27 km has been reported (Randel and Thompson, 2011;
Gebhardt et al., 2014; Eckert et al., 2014). Figure 11a1 shows two strong
peaks at 60 % corresponding to altitudes of 21.5 and 25 km. The
QBO contribution is important and does not change much over the altitude
range 24 to 26.5 km, indicating that the maximum amplitude of the QBO
signature occurs in this altitude range, where the degree of QBO influence on
ozone variability is approximately invariant. Fadnavis and Beig (2009) found
the QBO maximum over tropics at approximately 26 km, while Eckert
et al. (2014) found this maximum at 25 km. The QBO signal on ozone
variability is linked to the downward propagation of zonal wind throughout
the time. This process leads to a positive (negative) ozone anomaly when in
the westerly (easterly) mode with a cycle of around 2 years (Randel and
Thompson, 2011; Lee et al., 2010; Butchart et al., 2003). Note that the
maximum contributions of the QBO over the tropical and subtropical regions
(Fig. 11b1 and c1) are less than 12 %. These results confirm the
latitudinal signature of the QBO which is expressed by a decrease in its
effect on ozone variability poleward. The inter-annual ozone anomaly linked
to the QBO time evolution can be positive or negative depending on the
altitude range for a given period due to the downward propagation pattern of
the QBO with time (Randel and Thompson, 2011). The responses of the QBO index
used are presented in Fig. 11 as (a2) equatorial, (b2) tropical and
(c2) subtropical regions. Over the equatorial region, the QBO index profile
exhibits positive values below 23 km and negative values above. These
results indicate a strong contribution of the QBO westerly regime to ozone
variability for equatorial regions below 23 km during the studied
period (1998–2012). In contrast, the easterly regime had more influence on
variability of ozone above altitudes of 23 km. The opposite case is
observed in the subtropics, where the QBO index profile exhibits positive
values for altitudes between 18 and 23 km and negative values between
altitudes of 23 to 27.5 km. Similar results have been reported by
Bourassa et al. (2014). The response amplitude obtained in this work over the
subtropics varies within ±5 %, whilst it is at ±7 % (by unit
of the normalized QBO index) over the equatorial region. These results
reaffirm the key role of the QBO in ozone variability highlighted in
equatorial regions in contrast to the subtropics.
Height profile in % of the QBO contribution of
(a1–c1) and response by unit of the QBO index
(a2–c2) as calculated by the Trend-Run model over
the equatorial (a1, a2), tropical (b1, b2) and
subtropical (c1, c2) regions. SDs associated with
contributions are presented in grey dotted lines.
ENSO contribution to ozone profile variability
ENSO contributions to ozone profile variability over the 15–30 km
altitude range are presented in Fig. 12 for the three regions
(a1) equatorial, (b1) tropical and (c1) subtropical. These calculations show
that ENSO events weakly affect subtropical
ozone levels. In contrast, the ENSO contribution and its associated response
to ozone variability appear to be important below an altitude of
25 km at tropical latitudes. These results are in good agreement with
the study by Sioris et al. (2014) in which a statistically significant ENSO
contribution was reported at an altitude of 18.5–24.5 km in the
tropics. Such results indicate that ENSO influence on ozone variability is
essentially focused on the troposphere and lower stratosphere. ENSO
contribution to the lower stratospheric ozone is highlighted by the
20–25 km altitude band with peaks toward 15 and 13 % over the
tropical (at 23 km) and equatorial (at 21 km) regions
respectively. The response linked to this contribution over the equatorial
region oscillates with amplitude, varying between -8 and 4 % (Fig. 12,
curve a2). Here the ENSO response values are negative below an altitude of
23 km and positive in the 23–26 km altitude range,
indicating that the multivariate ENSO index is in phase with ozone time
evolution above 23 km and in opposite phase below 23 km. As
reported in previous studies (Ziemke and Chandra, 2003; Rieder et al., 2013),
ENSO events contribute to the variation in tropopause height associated with
decrease/increase in TCO due to the enhancing/suppressing of tropical
convection. The results obtained in the equatorial region indicate that the
ENSO effect, associated with enhanced convection, generally affects the lower
part of the stratosphere. This leads to a decrease in ozone levels below
23 km, while the suppressed convection present generally results in
a positive response to ozone variance above 23 km.
Height profile in % of the contribution of ENSO
(a1–c1) and response by unit of the ENSO index (a2–c2) as
calculated by the Trend-Run model over the equatorial (a1, a2),
tropical (b1, b2) and subtropical (c1, c2) regions. SDs associated
with contributions are presented in grey dotted lines.
However, the amplitude obtained for the negative ENSO response is the
largest. These results suggest that ozone variability linked to ENSO events
is more sensitive to the enhanced equatorial convection in the UT–LS. In
a recent study, similar results were reported by Randel and Thompson (2011),
in which they observed a large negative change in ozone variance due to
enhanced Brewer–Dobson circulation over the tropics associated with ENSO
variability. In addition, the obtained ENSO response over the tropical region
(Fig. 12b2) is negative, indicating a decrease in ozone due to ENSO events
occurring during the study period. From the troposphere to approximately
20 km, the ENSO signal exhibits an important contribution and
response to ozone variability over the tropics as reported in previous
published works (Bourassa et al., 2014; Sioris et al., 2014; Randel and
Thompson, 2011). However, the response obtained has a larger amplitude
(maximum of -38 % at 16.5 km) than those reported by the
above-mentioned papers due to the location of the Fiji and Samoa stations
(the two sites that represent the tropical region in this work). As mentioned
above, Fiji and Samoa are located in the eastern Pacific, a region where ENSO
events are significant. Here the ENSO contribution to total ozone variability
is observed to be greater than 10 % below 18.7 km. The negative
ENSO response associated with this contribution is explained by an enhanced
tropical upwelling during ENSO events which led to a decrease in ozone levels
in the lower stratosphere of the eastern Pacific Ocean region as reported by
previous studies (Randel and Thompson, 2011; Randel et al., 2009; Calvo
et al., 2010).
The trend estimates
Total column ozone trends
The trend analysis of TCO was performed for each site and results presented
in Fig. 13. The TCO trends are assessed based on the Trend-Run model and
expressed in percentage per decade. The obtained trend values are positive,
suggesting an increase in TCO during the study period. However, the mean
values of TCO trends obtained from the Samoa site (14.13∘ S)
equatorward are lower than 1 %, while they are higher than 1.5 % for
Fiji (18.13∘ S), Reunion (21.06∘ S) and Irene
(25.90∘ S). These results illustrate that the increase in TCO in the
subtropics is greater than in the tropics. Figure 14 summarizes the trend
evolution over the three latitudinal bands. An increase in the trend with
latitude southward is observed. The average trends (in percentage per decade)
are +0.74±0.6, +1.55±0.4 and +1.74±0.40 (±1σ)
over the equatorial, tropical and subtropical regions respectively. These TCO
trend results are consistent with the WMO (2014), where a positive trend of
approximately 1±1.7 (±2σ) was reported at
60∘ N–60∘ S for the period 2000–2013. These positive
trends are probably linked to the decline of effective equivalent
stratospheric chlorine (EESC) over the globe as supported by previous studies
(Yang et al., 2006; Anderson et al., 2000; Waugh et al., 2001; WMO 2010).
However, compared with the tropics and subtropics, the delay in ozone
improvement observed in the equatorial region is probably due to the tropical
strengthening of the Brewer–Dobson circulation (WMO 2014; Randel and
Thompson, 2011).
Time evolution of monthly total ozone values (blue) observed
at each site. The black lines represent the time evolution of the
TCO obtained by the Trend-Run model, the blue lines show observation data,
and the straight black line illustrates the obtained decadal
trend.
The same as Fig. 13 but per latitudinal region:
equatorial (a), tropical (b) and
subtropical (c).
It should be noted that approximately 10 % of ozone is tropospheric and
is produced as a result of chemical reactions between species such as
nitrogen oxide (NOx), carbon monoxide (CO) or volatile organic
compounds (VOCs). In the context of climate change due to anthropogenic
pollutant emissions and in spite of the efforts by the international
community to reduce GHG emissions, pollution has continued to increase in the
Southern Hemisphere, leading to a systematic increase in tropospheric ozone.
Thompson et al. (2014) assumed that growth in ozone precursors like
NOx and VOC may account for the characteristic free tropospheric
ozone increase, especially in wintertime. The observed positive trend in TCO
time series is therefore probably due also to an increase in and long-range
transport of pollutants (mainly from industrial and biomass burning activity)
in the free troposphere of the southern tropics and subtropics, as suggested
by previous studies (Diab et al., 2004; Thompson et al., 2014; Clain
et al., 2009). Thompson et al. (2014) highlighted significant increases
throughout the free troposphere, 20–30 % decade-1 over Irene and up
to 50 % decade-1 over Reunion in the southern subtropical region.
Furthermore, Heue et al. (2016) showed that the tropical tropospheric ozone
trend is 0.7±0.12 DU (approximately 3.5 %) decade-1 between
1995 and 2015. There results show that there is an improvement in ozone from
the ground to the middle atmosphere in the studied regions. Tropospheric
ozone affects total column ozone trends; however, the contribution is not
sufficient to modify the TCO trend compared to stratospheric ozone, which
represents 90 % of the total ozone present.
Vertical distribution of the trends
In this investigation, TCO analysis indicated a positive trend over the study
region. However, a trend investigation in terms of vertical ozone
distribution is necessary in order to understand trend variation through
different altitude levels. Equatorial, tropical and subtropical ozone trend
profiles estimated by the Trend-Run model are presented in Fig. 15, panels
(a), (b) and (c) respectively. Considering the temporal coverage of Irene
ozonesonde data (1998–2008 and some profiles recorded in 2012), the Irene
and Reunion ozone profiles were separated for the sake of trend analysis.
Figure 15c1 shows the ozone trend profile for Reunion, while that for Irene
(January 1998 to December 2008) is shown in panel c2. Overall, the ozone
trends obtained are negative in the upper troposphere and positive for
altitudes higher than 22 km. This indicates an improvement in
stratospheric ozone. For the equatorial region, the trend profile shows
a positive gradient from 15 km to the tropopause (approximately
18 km), followed by a maximum positive ozone trend in the
18–22 km layer (about +2 %). A negative trend is seen between
22 and 25 km and a zero trend for heights above 25 km. The
tropical trend evolution below 22 km is similar to the trend observed
over Reunion (Fig. 15c1), which is marked by a negative peak around
19 km. Such a negative peak is probably a characteristic of the
tropical trend and highlighted at Reunion due to the proximity of this
station to the tropical zone.
Vertical profiles of ozone decadal trends derived by the
Trend-Run model from 15 to 30 km at the
Equator (a), tropics (b),
Reunion (c1) and Irene (c2). Irene and Reunion are
separated for this present case due to the lack of ozone profiles at the
Irene station from January 2008 to October 2012. The Irene trend profile
is derived based on ozone profiles recorded from January 1998 to
December 2007 (10 years).
The results in trend estimates over the equatorial region are similar to
previous studies based on a combination of GOMOS and SAGE II data recorded
between 1984 and 2011 performed by Kyrölä et al. (2013). This work
showed an apparent improvement in ozone trends onwards from 1997. The
recorded trend (1997–2011) in the 20–30 km altitude range is
consistent with results obtained in the present study and can be summarized
as follows: a positive trend of approximately 2 % in the altitude range
19–21 km and approximately 0 % between altitudes 21 and
30 km. A trend varying between 0 and 1 % was also observed by
Randel and Thompson (2011) in an altitude range of 23 to 35 km by
averaging SHADOZ and SAGE II ozone profiles recorded between 1984 and 2009
for latitudes 20∘ N–20∘ S. Furthermore, Randel and
Thompson (2011) observed a negative peak at 19 km, similar to what
was observed in this study over Reunion and the tropical region. This
negative peak is explained as resulting from a systematic increased tropical
lower stratospheric upwelling. As reported by the WMO (2014), the increase in
tropical upwelling is associated with a strengthening of the Brewer–Dobson
circulation caused by GHG-induced climate change. Furthermore, Lamarque and
Solomon (2010) associated the declining trend in ozone levels in the lower
tropical stratosphere with an increase in carbon dioxide and sea surface
temperature. They also demonstrated that a decrease in ozone in the upper
stratosphere could be strongly linked to an increase in halogen compounds at
high altitudes. The results obtained in this work indicate that the positive
trends observed over Reunion and Irene from TCO analyses are probably due to
an increase in ozone levels in the stratosphere, which is associated with
a decrease in halogen compounds and EESC at high altitudes. Declining EESC
levels in the subtropical region constitutes the main reason for ozone
increase as a result of the Montreal Protocol and its amendments (Yang
et al., 2006; Anderson et al., 2000; Reinsel, 2002; WMO, 2010, 2014).
The positive trend of 2 % in ozone levels recorded over the equatorial
region in the 18–22 km altitude range cannot be explained by the
decline of EESC compounds in the stratosphere. Instead, this trend could
rather be linked to an increase in NOx (NO+NO2) in this
altitude range (Gebhardt et al., 2014; Nevison et al., 1999). Furthermore,
Gebhardt et al. (2014) obtained a positive ozone trend at altitudes higher
than 18 km and the variation pattern observed is similar to that
obtained in our study up to an altitude of approximately 23 km. The
investigation of Gebhardt et al. (2014) was based on SCIAMACHY satellite
observations (2002–2012). They associated the observed positive trend at
18–22 km with an increase in NOx concentration in the
lower stratosphere. The idea that NOx levels may have a direct
effect on stratospheric ozone variability is not new and was first suggested
by Nevison et al. (1999). In this study they used a model in order to
identify the NOx contribution to ozone variability at different
altitudes in the stratosphere. Through these calculations it was found that
the increase in NOx compounds in the lower stratosphere leads to an
increase in ozone levels at this altitude. It is important to note that
NOx can have a buffering effect on the Clx and HOx
ozone-destruction cycles in the tropical lower stratosphere as explained by
Nevison et al. (1999).
Conclusions
This work describes and discusses the variability and associated trends in
ozone measurements recorded between January 1998 and December 2012 over
eight southern tropical and subtropical sites. Total ozone products from
different instruments were presented and a very good agreement was found in
products recorded by different instruments at the same site. This confirmed
the good quality of products and the validity of combining these products in
order to create a long-term and homogenous TCO dataset for the study of ozone
variability and long-term trends over the southern tropics and subtropics.
SHADOZ data, used for the study of vertical ozone distribution, were found to
be good quality according to Thompson et al. (2003a, b). The analysis of the
contribution and response of some parameters to total ozone variance was
achieved through the use of a multi-regression model called Trend-Run. This
model is able to describe 71–91 % of ozone variability influenced by
seasonal variation of climate and by dynamic parameters such as QBO, ENSO and
solar flux. The obtained results were found to be in good agreement with
recent findings and can be summarized as follows.
Ozone variability over the study region is dominated by annual
oscillation (AO) which strongly affects ozone
variance in the UT–LS as a result of seasonal troposphere–stratosphere
exchanges. The amplitude of ozone annual oscillations is high in the
subtropics and decreases
with latitude equatorward. In contrast, the ozone SAO is more
apparent and contributes significantly above 27 km, with its maximum
influence at the lower latitudes (around 8∘ S), where its amplitude
decreases with movement toward the Equator or
subtropics.
The QBO signal constitutes the second most important parameter
in terms of effect on ozone variability in the tropics after the
seasonal oscillation. QBO heavily affects ozone
variance in 20–28 km of the altitude range, with its maximum
amplitude in the equatorial region. The pattern of the QBO response to ozone
variability in the equatorial region is out of phase with that of the
subtropics. The
border between equatorial and subtropical patterns is found at
around 15∘ in latitude.
The ENSO contribution over the subtropics is less than
1 %. In contrast, an important contribution of ENSO to ozone
variance is highlighted in the tropics, below 25 km with its
maximum amplitude in the Pacific area. The tropical ENSO response is
basically negative and was explained in previous work (Randel and
Thompson, 2011; Sioris et al., 2014) as resulting from an
enhancement of tropical convection leading to ozone decrease during
warm ENSO events.
Solar flux and total ozone are in phase. Increase in ozone
levels occurs when sunspot intensity increases and conversely periods of low
sunspot activity correspond to periods of low ozone production. The maximum
solar flux contribution is at the tropical
latitudes. The mean contribution of solar flux to total ozone
variability over the studied region is high (6.03±0.84 %)
compared to the 3 % reported globally by the WMO (2010, 2014). This
is probably due to the region selected for this study (tropics and
subtropics), the length of the data records (less than two solar cycles) and
the time period under investigation in this work (1998–2012).
The regression model is also used to quantify ozone trends over the selected
regions. Results obtained exhibit an upward trend of TCO during the period of
study. The trend values were in good agreement with the WMO (2014) report (1±1.7 (2σ) at 60∘ N–60∘ S for the period
2000–2013) and exhibited an increase in ozone with increase in latitude from
the Equator to the subtropics. The results of vertical profile analysis
illustrated a positive ozone trend from 22 km upward and negative
values in the upper troposphere, indicating that the ozone recovery observed
in tropical and subtropical regions occurs in the stratosphere. It has been
explained in recent studies (Sioris et al., 2014; Bourassa et al., 2014;
Randel and Thompson et al., 2011) that the delay in ozone recovery at upper
tropospheric and lower stratospheric levels was partly associated with
a tropical enhancement of the Brewer–Dobson circulation over the tropics,
while ozone recovery observed from an altitude of 22 km and upward
was probably linked to declining levels of stratospheric chlorine and bromine
compounds in the atmosphere. A further study continuing from this work would
involve the inclusion of Brewer–Dobson and EESC indexes in the model not
only to quantify the sources of observed trends, but also to improve the
model quality.