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\title{Global and zonal tropospheric ozone variations from 2003--2011 as seen by SCIAMACHY}

\Author[1]{F.}{Ebojie}
\Author[1]{J.~P.}{Burrows}
\Author[1]{C.}{Gebhardt}
\Author [1]{A.}{Ladst\"{a}tter-Wei{\ss}enmayer}
\Author[2]{C.}{von~Savigny}
\Author[1]{A.}{Rozanov}
\Author[1]{M.}{Weber}
\Author[1]{H.}{Bovensmann}

\affil[1]{Institute of Environmental Physics (IUP), University of Bremen, P.O. Box 330440, 28334 Bremen, Germany}
\affil[2]{Institute of Physics, Ernst-Moritz-Arndt-University of Greifswald, Felix-Hausdorff-Str. 6, 17489 Greifswald, Germany}

\runningtitle{Global and zonal tropospheric ozone variations from 2003--2011 as seen by SCIAMACHY}

\runningauthor{F.~Ebojie et~al.}

\correspondence{F.~Ebojie (felix@iup.physik.uni-bremen.de)}

\received{8~July 2015}
\accepted{10~August 2015}
\published{}

\firstpage{1}

\maketitle

\begin{abstract}

  An analysis of the tropospheric ozone (\chem{O_3}) columns (TOCs)
  derived from SCIAMACHY limb-nadir-matching (LNM) observations during the period  2003--2011, focusing on zonal and global variations in TOC, is
  described.  The changes are derived using a multivariate linear
  regression model.  TOC shows changes of $-0.2 \pm
  0.4$\,{{\%}}\,\unit{yr^{-1}}, $0.3 \pm 0.4$\,{{\%}}\,\unit{yr^{-1}},
  $0.1 \pm 0.5$\,{{\%}}\,\unit{yr^{-1}} and $0.1 \pm
  0.2$\,{{\%}}\,\unit{yr^{-1}}, which are not statistically
  significant at the 2\,$\sigma$ level in the latitude bands
  30--50{\degree}\,N, 20{\degree}\,S--0, 0--20{\degree}\,N and
  50--30{\degree}\,S, respectively.  Tropospheric \chem{O_3} shows
  statistically significant increases over some regions of South Asia
  (1--3\,{{\%}}\,\unit{yr^{-1}}), the South American continent (up to
  2\,{{\%}}\,\unit{yr^{-1}}), Alaska (up to 2\,{{\%}}\,\unit{yr^{-1}}) and around
  Congo in Africa (up to 2\,{{\%}}\,\unit{yr^{-1}}).  Significant increase in
  TOC is determined off the continents including
  Australia (up to 2\,{{\%}}\,\unit{yr^{-1}}), Eurasia
  (1--3\,{{\%}}\,\unit{yr^{-1}}) and South America (up to
  3\,{{\%}}\,\unit{yr^{-1}}).  Significant decrease in TOC (up to
  $-$3\,{{\%}}\,\unit{yr^{-1}}) is observed over some regions of the
  continents of North America, Europe and South America.  Over the
  Oceanic regions, significant decrease in TOC of about
  $-$2\,{{\%}}\,\unit{yr^{-1}} is observed between Europe and
  North America. In addition, the response of El Ni\~{n}o-Southern Oscillation and quasi-biennial oscillation to changes in TOC for the period 2003--2011  was investigated. The result shows extensive regions of significant ENSO responses to changes in TOC and significant QBO response to TOC changes over some regions. 

\end{abstract}

\introduction

Tropospheric ozone (\chem{O_3}) is a~major environmental concern. The
amount of tropospheric \chem{O_3} has been shown to be increasing
globally during the 20th century due to enhanced emissions of
anthropogenic precursors
\citep{Marenco1994,Shindell2006}. Tropospheric \chem{O_3} is an
important greenhouse gas and a~pollutant.  It affects air quality and
contributes to global warming \citep[e.g.,][]{Shindell2006,
  Jacob-Winner2009}. It is responsible for respiratory diseases in
humans, leads to premature mortality and causes damage to crops and ecosystems
\citep[e.g.,][]{Lippmann1991,Jacobson2012}.  Ozone is a~strong oxidant
and a~precursor of hydroxyl (OH) and hydroperoxy (\chem{HO_2})
radicals, which drive tropospheric chemistry \citep{Levy1971,
  Logan1981}.  Tropospheric \chem{O_3} has two main sources:
photochemical production within the troposphere and transport from the
stratosphere. The photochemical source of tropospheric \chem{O_3} is
mainly controlled by emissions of \chem{O_3} precursors including
nitrogen oxides (\chem{NO}$_x$) and volatile organic compounds (VOCs)
\citep{Haagen-Smit1952,Lelieveld_Dentener2000}.  Instantaneous rates
of formation of \chem{O_3} are dependent on the relation between
\chem{O_3}, \chem{NO}$_x$ and VOC from which two regimes with
different \chem{O_3}-\chem{NO}$_x$-VOC sensitivity can be identified,
i.e.  a~\chem{NO}$_x$-sensitive regime (limited \chem{NO}$_x$ and high
VOC) and a~VOC-sensitive regime (saturated \chem{NO}$_x$ and low
VOC). In the \chem{NO}$_x$-sensitive regime, \chem{O_3} concentrations
increase with increasing \chem{NO}$_x$ and exhibit little response to
increasing VOC. In the VOC-sensitive regime, \chem{O_3} concentrations
decrease with increasing \chem{NO}$_x$ and increase with increasing
VOC. While the \chem{NO}$_x$-sensitive regime is associated with high
$\text{VOC}/\text{NO}_x$ ratios, the VOC-sensitive regime is
associated with low $\text{VOC}/\text{NO}_x$ ratios. The
\chem{O_3}-\chem{NO}$_x$-VOC sensitivity varies with location, events
and time. The VOC-sensitive chemistry is more likely in urban
or pollution source regions, while \chem{NO}$_x$-sensitive chemistry
is more likely downwind \citep{Milford1989, Duncan2010}.  \chem{O_3}
precursors such as \chem{NO}$_x$ and VOCs are emitted from fossil fuel
combustion, power plants, biomass burning, lightning and other
combustion sources. They are also emitted from microbes in the soil
and from vegetation \citep{Guenther2000}. High amounts of \chem{O_3}
precursors anthropogenically produced can accumulate to a~hazardous
level during favourable meteorological conditions \citep{Zhang1998}.
The reaction of carbon monoxide (CO), VOCs, and methane (\chem{CH_4})
with OH radicals is the main source of peroxy (R\chem{O_2},
\chem{HO_2}) radicals, which facilitate the conversion of nitric oxide
(NO) to nitrogen dioxide (\chem{NO_2}). Hence, a~decrease in the
chemical fuels (CO, VOCs, \chem{CH_4}) may ultimately lead to
a~decrease in the production of \chem{NO_2}. Increase in tropospheric
\chem{NO}$_x$ concentrations can lead to increases in
\chem{O_3}. However, under conditions of high \chem{NO}$_x$ (reaching levels beyond 10\,\unit{ppb}), which are found mainly in pollution source regions in some urban centers and around power plants,
\chem{O_3} can be lost or titrated via the reaction: NO\,$+ \chem{O_3}
\to \chem{NO_2} + \chem{O_2}$ \citep[e.g.,][]{Sillman1990,Brasseur1999}.  Nevertheless, \chem{O_3}
is reformed downwind in the plume from urban areas
\citep[e.g.,][]{Crutzen1979}.

The influx of \chem{O_3}-rich stratospheric air through stratosphere-troposphere exchange (STE) also
contributes to tropospheric \chem{O_3} abundance. \chem{O_3} is
transported from the lower stratosphere into the upper troposphere
through tropopause foldings \citep{Danielsen1968,Fusco-Logan2003} and
it is exchanged with the troposphere via diabatic processes and
turbulent diffusion \citep{Lamarque-Hess1994}, mixing processes and
convective injection during the breakup of stratospheric filaments
\citep[e.g.,][]{Appenzeller1996}.

Chemical removal of tropospheric \chem{O_3} occurs by photolysis and
catalytic reactions involving hydrogen and oxides of hydrogen (OH\,$+
\chem{HO_2}$) as well as halogen and halogen oxides
\citep{Finlayson-Pitts1990,Monks2005,Gentner2014}. In addition,
\chem{O_3} oxidizes VOCs with double bonds (e.g., isoprene, methanol,
acetone, etc.) and is also removed from the troposphere by deposition
to the surface.  The photolysis of \chem{O_3} plays a~key role in
tropospheric chemistry and it is strongly determined by the
stratospheric \chem{O_3} column. It has been observed that a~negative
trend in stratospheric \chem{O_3} enhances the \chem{O_3} photolysis
in the troposphere as a~result of increasing UV-B radiation
\citep{WMO1999}.  The combination of sources, loss processes and
transport within the troposphere determines the amount of \chem{O_3}
at any given time and location within the troposphere i.e., its
spatial and temporal distribution and variability.

The factors contributing to trends in background \chem{O_3} include
changes in emissions of \chem{O_3} precursors, meteorological
variability (changes in temperature and solar radiation), and regional and
intercontinental transport of \chem{O_3} and its precursors
\citep{Jacob1999, Liu2002}. Changes in circulation and stratospheric
ozone intrusions also play a~role
\citep[e.g.,][]{Dickerson1995,Cooper_Parrish2004}. Furthermore, long-range ozone transport within the troposphere is modulated by interannual to decadal climate variability \citep[e.g.,][]{Lin2014,Ziemke2015}.

In the 20th century, the annual mean concentrations of tropospheric
\chem{O_3} at the northern mid and high latitudes were found to have
more than doubled due due to anthropogenic activities \citep{Marenco1994,Shindell2006}.  Although
\chem{O_3} mitigation efforts in the developed countries have led to
a~decrease in peak \chem{O_3} levels in both urban and rural areas and
led to negative trends on a~regional scale, particularly over Europe
and North America in recent years \citep{Naja2003,Derwent2003,Cooper2014,Parrish2013,Parrish2014},
background levels continue to increase in some regions
\citep{Oltmans2006}.  Furthermore, \chem{O_3} concentrations over some
developed and developing countries are rising due to increased
emissions of \chem{NO}$_x$ and other \chem{O_3} precursors from
anthropogenic activities \citep[e.g.,][]{Dentener2005}.  As a~result
of regional and intercontinental transport of \chem{O_3}
\citep[e.g.,][]{Fiore2009,Parrish1993}, increase in \chem{O_3} is not
only limited to local/regional sources but also hemispheric scale
sources \citep{Tor2003}. Therefore, reductions in local or national emissions of \chem{O_3}
do not always produce an analogous decrease in atmospheric concentrations.

Trends in tropospheric \chem{O_3} have been derived from measurements
from urban and rural sites \citep[e.g.,][]{Oltmans2006,Oltmans2013,Cooper2012},
ozonesondes and commercial aircrafts \citep[e.g.,][]{Logan2012}. Ozone
trends derived from satellite measurements have been published
\citep[e.g.,][]{Ziemke2005,Beig2007}. Some of the TOC trends based on
satellite measurements are derived from different instruments with
differing algorithms and sampling characteristics and this can
introduce some uncertainties in the data sets \citep{Beig2007,
  Doughty2011}. Most of the past tropospheric \chem{O_3} trend studies
focused on regional pollution in the Northern Hemisphere (NH), where
\chem{O_3} precursors are well characterized
\citep[e.g.,][]{Logan2012, Parrish2012}. The goal of this study is to
determine the zonal and global changes in the tropospheric \chem{O_3}
data product derived from SCIAMACHY limb-nadir-matching (LNM)
observations during the period 2003--2011 \citep{Ebojie2014,Ebojies2014}.  The manuscript is
organized as follows: Sect.~\ref{data_model} briefly describes the
data and regression model used in this
study. Sect.~\ref{result_discussion} provides results and
discussion, and Sect.~\ref{conclusions} summarizes principal findings
and conclusions.

\section{Data and regression model}
\label{data_model}

In this work, the tropospheric ozone column (TOC) time series derived
from SCIAMACHY limb and nadir measurements for the period of
2003--2011 using the LNM technique \citep{Ebojie2014,Ebojies2014}, have been used
to investigate their zonal and global linear trends.  The LNM
technique is a~residual approach that subtracts stratospheric
\chem{O_3} columns (SOCs), retrieved from the limb observations, from
the total \chem{O_3} columns (TOZs), derived from the nadir
observations. The technique requires accurate knowledge of the SOCs,
TOZs, tropopause height, and their associated errors. The SOCs were
determined from the stratospheric \chem{O_3} profiles retrieved in the
Hartley and Chappuis bands of \chem{O_3} from SCIAMACHY limb
scattering measurements. The TOZs were also derived from SCIAMACHY
measurements, but in this case from the nadir viewing mode using the
Weighting Function Differential Optical Absorption Spectroscopy
(WFDOAS) technique in the Huggins band of \chem{O_3}.  Since TOCs
constitute only about 10\,{{\%}} of the TOZs, small errors associated
with clouds might significantly affect the derived TOCs. Therefore,
limb scenes that are contaminated with clouds and nadir measurements
that have a~cloud fraction of more than 0.1 were screened out.
The TOCs derived from SCIAMACHY LNM observations were thoroughly validated by comparison with a large suite of correlative TOC data sets derived from balloon-borne platforms and concurrent satellite measurements. The results of this extensive validation are described in \citet{Ebojies2014} and \citet{Ebojie2014}. These studies demonstrate the high quality of SCIAMACHY TOC and show no observable drifts in the validation with other tropospheric \chem{O_3} data sets. However, further improvement for the limb vertical profile may reduce the error on the TOC. The SCIAMACHY TOCs show good agreement with TOCs derived from other satellite instruments and comparison with co-located ozonesonde measurements shows agreement to within 3 DU (Dobson Units) on average \citep{Ebojie2014,Ebojies2014}. Detailed discussion of the LNM technique applied to the retrieval of TOC from SCIAMACHY, a sensitivity study and validation of the SCIAMACHY TOC are given in \citet{Ebojies2014} and \citet{Ebojie2014}.

The SCIAMACHY tropospheric \chem{O_3} changes are determined using
a~multivariate linear regression taking the autocorrelation of
consecutive values into account. This helps to obtain more realistic
uncertainties on the TOC trends, thus giving a~better understanding of
what drives \chem{O_3} trends and variability.
The implemented approach is
similar to that used for stratospheric \chem{O_3} trends and
variability by \citet{Gebhardt2014}.  The regression model is
expressed as:
\begin{align}
\label{linear_reg}
Z_{t} = \mu + \omega{t} + S_t + R({t}) + \epsilon(t),
\end{align}
where $Z_{t}$ is the monthly mean tropospheric \chem{O_3} data, $t$ is
the month index (1--108), $\mu$ is the intercept, $\epsilon$ is the
error term, $\omega$, which is the gradient or slope, represents the
linear trend.  $S_t$ is the seasonal component including annual, semi-annual
and the 3- and 4-month harmonic terms as shown in Eq.~(\ref{sterm}).

\begin{align}
\label{sterm}
S_{t} = \sum\limits_{i=1}^4{\left(\beta_{1i}\sin\left(\frac{2\pi{it}}{12}\right) + \beta_{2i}\cos\left(\frac{2\pi{it}}{12}\right)\right)}.
\end{align}
$\beta_{1i}$ and $\beta_{2i}$ are parameters, which are determined to
fit the amplitude and phase of the oscillations \citep[e.g.,][]{Clarmann2010,
  Eckert2014}. The harmonics (12, 6, 4, 3\,month) are used to
represent seasonal changes \citep{Weatherhead2000}. The 12 and 6
months correspond to the annual and semi-annual cycles, respectively.
The terms with the 3- and 4-month periods help to improve the fit quality
mostly when the cycle shape is not sinusoidal \citep{Stiller2012}. The
combination of sine and cosine terms provide a~flexible adjustment to
any phase of the periodic variation.  $R({t})$ represents
additional terms including the quasi-biennial oscillation (QBO) and El
Ni\~{n}o-Southern Oscillation (ENSO), which can be expressed as:
\begin{align}
R(t) = m{\text{QBO}}_{50}(t) + n{\text{QBO}}_{70}(t) +
x\left(\chem{N_{34}}(t) + \frac{\mathrm{d}\chem{N_{34}}(t)}{\mathrm{d}t}\Delta({t})\right).
\label{rterm2}
\end{align}
The first and second terms of Eq.~(\ref{rterm2}) are the QBO terms,
while the last term corresponds to the ENSO term. The factors $m$, $n$
and $x$ represent their fit coefficients. The QBO indices of 50 and 70
denote the pressure level of the proxy (in hPa). The solar term is not included
in the regression model because the residuals show no evidence of
a solar-cycle response.  The different contributions to the
regression model are described in more detail in
Sect.~\ref{qboscenso}.  The trend uncertainty is 1\,$\sigma$, which is
defined by the covariance matrix of the regression coefficients. The
trends and their uncertainties are presented in relative units of
{{\%}}\,\unit{yr^{-1}}, i.e., percent per year relative to the mean
value of the underlying time series. The reported changes in TOC are
considered statistically significant at the 95\,{{\%}} confidence
level if the absolute ratio of the trend to its uncertainty is greater
than 2 \citep{Tiao1990}.

\subsection{Quasi-biennial oscillation and El Ni\~{n}o-Southern Oscillation signatures in ozone}
\label{qboscenso}

El Ni\~{n}o-Southern Oscillation (ENSO) and quasi-biennial oscillation
(QBO) influence tropospheric \chem{O_3} through modulation of the
stratospheric circulation \citep[e.g.,][]{Simpson2011,Baldwin2001},
stratospheric \chem{O_3} \citep[e.g.,][]{Oman2013}, and STE \chem{O_3} influx
\citep[e.g.,][]{Zeng2005}.  QBO is characterized by alternating
easterly and westerly wind regimes within a~variable period of
2--3\,\unit{years} in the tropics \citep{Pascoe2005}.  The QBO directly modulates
tropical upwelling through the thermal wind balance.  Although the QBO
is a~tropical phenomenon, it influences the entire stratospheric flow
by modulating the effects of extratropical waves. It regulates the
upwelling branch of the Brewer--Dobson circulation (BDC) and leads to
variation in \chem{O_3} volume mixing ratio (VMR) mostly at the
altitudes of steep \chem{O_3} VMR gradient, which is found below and
above the maximum in the vertical \chem{O_3} VMR profiles
\citep[][]{Butchart2003}.  QBO has been identified to affect the
troposphere indirectly through its effect on the polar vortex
\citep[e.g.,][]{Holton1980}. It directly affects the tropical and
extratropical troposphere through convective anomalies
\citep{Haigh2005,Neu2014}. Evidence of a~QBO signal in the tropical
troposphere \chem{O_3} has been recorded from satellite and ozonesonde
measurements \citep[e.g.,][]{Chandra2002, Lee2010}.  QBO can also
affect the troposphere through tropospheric eddies even in the absence
of an anomalous polar vortex or tropical convection
\citep[e.g.,][]{Garfinkel2011}.  A~downward propagating QBO \chem{O_3}
signal has been found to extend to the mid-troposphere where the phase
analysis of the temperature anomalies implies that the driving force
is a~zonal mean overturning circulation associated with thermal wind
adjustment \citep{Lee2010}.  In the present study, the 50 and 70 hPa
Singapore (1{\degree}\,N, 140{\degree}\,E) winds
(\url{http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/}, last
access: 28~April~2015) are used as fit proxies. They are smoothed by
a~4\,month running mean and used globally in the form of
Eq.~(\ref{eqbo1}).
\begin{align}
\label{eqbo1}
{\text{QBO}}(t) = m{\text{QBO}}_{50}(t) + n{\text{QBO}}_{70}(t).
\end{align}

Another dominant driver of interannual tropospheric variability is
ENSO with a~varying period of 2--7\,\unit{years} and maximizes around
late boreal autumn to late winter \citep{Bjerknes1966}.  The response
of tropospheric \chem{O_3} to ENSO has been demonstrated in earlier studies
\citep{Chandra1998, Lad1999,Zeng2005, Valks2014, Ziemke2014,
  Coldewey2014}.  Response to circulation and convective changes
during El Ni\~{n}o lead to decrease in TOC in the central and East
Pacific and an increase in the West Pacific/Indonesia
\citep{Fujiwara1999,Thompson2001,Valks2014, Ziemke2014}.
Statistically significant signals consistent with anomalous vertical
motions during ENSO have been observed from tropospheric \chem{O_3}
profiles associated with the Southern Oscillation Index (SOI)
\citep{Lee2010}.  In this study, an ENSO proxy based on the anomalies
of the Ni\~{n}o 3.4 index
(\url{http://www.cpc.ncep.noaa.gov/data/indices/},last
access: 30~April~2015) is employed to
reveal ENSO signatures in tropospheric \chem{O_3} time series. The
combined fit of the proxy and its time derivative accounts for the
time lag as shown in Eq.~(\ref{enso}).
\begin{align}
\label{enso}
{\text{ENSO}}(t) = x\left(\chem{N_{34}}(t) + \frac{\mathrm{d}\chem{N_{34}}(t)}{\mathrm{d}t}\Delta({t})\right),
\end{align}
where \chem{N_{34}} is the ENSO proxy and $\Delta({t})$ is the time
lag. Therefore, to account for the non-linearity of $\frac{\mathrm{d}\chem{N_{34}}(t)}{\mathrm{d}t}$, for selected cases, fits were repeated using time lags varying  from 0 to 2 months. It was found that best results were obtained using a time lag of X months, and this time lag was then adopted for all fits.

% Therefore, to account for the non-linearity of
% $\frac{\mathrm{d}\chem{N_{34}}(t)}{\mathrm{d}t}$, the fit starting
% with a~time lag of 2 months is repeated with the lagged proxy until
% the remaining time lag approaches zero. The iteration begins at a time lag of 2 months and the standard for omitting ENSO terms in the regression analysis is based on the time lag of the proxy that does not fall at a certain value (including zero).



\section{Results and discussion}
\label{result_discussion}
\subsection{Global distribution of tropospheric \chem{O_3}}

Analysis of the global distribution of tropospheric \chem{O_3} is
important for the understanding of recent changes in \chem{O_3} and
its precursor emissions. The tropospheric \chem{O_3} distribution
reflects a~balance between local production and destruction as well as
atmospheric transport. Figure~\ref{season_oz} shows the seasonal
distribution of the global tropospheric \chem{O_3} in DU retrieved
from SCIAMACHY observations averaged over 2003--2011 for 5{\degree}
latitude\,$\times$\,5{\degree} longitude bins \citep{Ebojies2014,Ebojie2014}.  Seasonal averages of
the TOC provide a~good compromise between minimizing variability
associated with shorter time period averages and thus, reveal
information on seasonal long-term \chem{O_3} changes \citep{Parrish2012,Ebojies2014}.

In the tropics, TOC in the top left panel of Fig.~\ref{season_oz} for
December--February shows a~characteristic tropospheric \chem{O_3}
wave-one feature, which is persistent in the southern tropics with
higher TOC values ($> 40$\,DU) over the southern Atlantic tropics than
the southern Pacific as reported elsewhere
\citep[e.g.,][]{Fishman1990,Ebojies2014}. This feature is observed in all seasons
and maximizes in September--November with tropospheric \chem{O_3}
values greater than 40\,DU as compared to TOC values of less than
30\,DU over the southern Pacific \citep{Ebojies2014}. The wave-one pattern in TOC
originates from different contributing factors including biomass
burning and lightning in both Africa and South America, deep
convection in the Pacific region coupled with vertical injection of
low marine boundary layer tropospheric \chem{O_3} into the middle and
upper troposphere (the large scale Walker Circulation)
\citep[e.g.,][]{Fishman1990,Chandra2003,Sauvage2007}. In the top right
panel of Fig.~\ref{season_oz}, which corresponds to TOC for
June--August, largest TOC values ($> 45$\,DU) occur in the NH
extratropics with a~corresponding enhancement of TOC values ($>
35$\,DU) in the tropical/subtropical southern Atlantic region. The
high level of tropospheric \chem{O_3} during boreal summer is
primarily photochemically produced from anthropogenic pollution,
biogenic VOCs and \chem{NO}$_x$ \citep[e.g.,][]{Chandra2004}. Pollution effects with sources from
STE, biomass burning and lightning also play a~role during this season of
the year as reported by many sources
\citep[e.g.,][]{Pfister2008,Thompson2008,Chandra2004}. During
March--May, i.e., bottom left panel of Fig.~\ref{season_oz}, NH
springtime TOC buildup is observed. The high tropospheric \chem{O_3}
values ($>45$\,DU) in the northern subtropics and mid-latitudes are associated
primarily to STE processes, which have an annual maximum in these
zonal bands \citep[e.g.,][]{Chandra2004}. Also of importance to the
enhancement of \chem{O_3} in these zonal bands are tropospheric
\chem{O_3} production sources including lightning, combustion of
fossil fuels, biomass burning and soil emissions
\citep[e.g.,][]{Pfister2008,Thompson2008}. There is a~shift in the
seasonal cycle from summer maximum to spring/summer peak mostly over
northern midlatitudes which can be attributed to either
emission reductions over the region or an increase in springtime
\chem{O_3} or both as reported elsewhere \citep[e.g.,][]{Parrish2013, Cooper2014}. In the bottom
right panel of Fig.~\ref{season_oz}, corresponding to TOC for
September--November, the high TOC values ($>40$\,DU) that lie along a~zonal band
of approximately 30--50{\degree}\,S in the Southern Hemisphere (SH)
are similar to the high values observed at the NH mid-latitudes
during summer and spring. These seasonal enhancements in TOC are of
dynamical origin caused by STE \citep[e.g.,][]{deLaat2005}.

\subsection{Global wind distribution}

A~good knowledge of the global wind distribution is necessary for the
interpretation of changes in global tropospheric \chem{O_3} as it
reveals information on the continental outflow and intercontinental
transport of \chem{O_3} pollution.  Variation in wind speed and
direction can favour tropospheric \chem{O_3} variability. Changes in
tropospheric \chem{O_3} are not only a~consequence of chemical
reactions but are also greatly affected by tropospheric circulation.
Figure~\ref{season_wd} shows contour plots of the global distribution
of the seasonal wind speed in \unit{m\,s^{-1}} at 500\,\unit{hPa}
averaged over the years 2003--2011, which is overlaid with arrows (in
blue) depicting wind direction. The plots are based on the ECMWF
(European Centre for Medium-Range Weather Forecasts) Reanalysis
Interim (ERA-I) wind data for 1.5{\degree}
latitude\,$\times$\,1.5{\degree} longitude bins regridded to
5{\degree} latitude\,$\times$\,10{\degree} longitude bins for better
identification of the magnitude and direction of the wind. The plots
show that in the free troposphere, the easterlies prevail in the
tropics while westerlies prevail in the extratropics.  Low
wind speeds of less than 9\,\unit{m\,s^{-1}} dominate the tropics with
wind speed of less than 6\,\unit{m\,s^{-1}} observed across part of
South America, the Atlantic Ocean, Africa, the Indian Ocean and the
maritime southeast Asia region. This shows the manifestation of the
ITCZ \citep[Intertropical Convergence Zone,][]{Trenberth2000}.

In the top left panel of Fig.~\ref{season_wd}, which corresponds to
the wind distribution for December--February, high wind speed greater
than 24\,\unit{m\,s^{-1}} is observed over the North Atlantic and
North Pacific Oceans.  A~corresponding high wind speed appears over the
southern extra-tropics, mostly in the 40--60{\degree}\,S latitude
bands.
During boreal summer (June--August), which is shown in the top
right panel of Fig.~\ref{season_wd}, large wind speed greater than
18\,\unit{m\,s^{-1}} is observed over the southern extra-tropics with
the largest wind speed greater than 24\,\unit{m\,s^{-1}} occurring in
the region 40--60{\degree}\,S and 35{\degree}\,W--100{\degree}\,E. There is less frequent cyclones in the northern
mid-latitude during boreal summer.
In the bottom left panel of Fig.~\ref{season_wd}, which corresponds to
the wind distribution for March--May, frequent storms similar to those observe in the NH of December--February are observed. Largest wind speed greater than 24\,\unit{m\,s^{-1}} are observed in the SH in the region 30--60{\degree}\,S and 30{\degree}\,W--180{\degree}\,E as well as in some regions of east Asia.
During boreal autumn (September--November), which is shown at the bottom right panel of Fig.~\ref{season_wd}, wind speed greater than 21\,\unit{m\,s^{-1}} occurs in some regions of the NH, with the largest wind speed greater than 24\,\unit{m\,s^{-1}} occurring in the SH  in the region 40--60{\degree}\,S and 20{\degree}\,W--100{\degree}\,E.
Similar wind patterns are observed in both hemispheres of
March--May and September--November as shown in the bottom left and
right panels of Fig.~\ref{season_wd}, respectively.
Some localized areas of high wind speed appear over the North Atlantic and North
Pacific around the Gulf of Alaska, which are produced by tropical
cyclones.  An asymmetry in the winter to summer variation between the
two hemispheres is observed. The largest seasonal variations in wind
speed occur in the NH oceans mostly over the northern Atlantic. In
contrast, the winter to summer variation in wind speed over the SH
oceans is relatively small.  The largest wind speeds greater than
24\,\unit{m\,s^{-1}} appear at the southern extratropics at all
seasons with corresponding large wind speed in the northern
extratropics during boreal winter, spring and autumn. These bands of
high wind speed are associated with mid-latitude winter storms \citep[e.g.,][]{Romanski2014}.
Sudden reversal or change in wind direction is observed in all seasons between latitudes
15{\degree} and 35{\degree} in both hemispheres. This shows the
manifestation of subtropical highs, which have been observed to have
shifted due to direct radiative forcing from greenhouse gases among
other processes \citep[e.g.,][]{Hudson2012}.

Wind distribution governs changes in the transport of \chem{O_3} and its precursors. For instance, the middle-upper tropospheric anticyclone due to response to the thermal and orographic forcing of the Tibetan Plateau has great impact
on the atmospheric circulation in the NH
\citep[e.g.,][]{Ye_Wu1998,Zhang2005}. The trans-Pacific transport of
Asian pollution can influence changes in \chem{O_3} over North America
and the Oceanic regions \citep[e.g.,][]{Jaffe1999,Parrish2012,
  Cooper2014}, variation in the trans-Atlantic transport of North
American pollution can influence pollution over Europe as well as the
Atlantic Oceanic region \citep[e.g.,][]{Derwent1998, Logan2012}, and
trans-Eurasian transport of European pollution can influence changes
in \chem{O_3} over Asia \citep[e.g.,][]{Liu2002,Parrish2012}.  The
vertical wind velocity in the middle troposphere plays a~significant
role in controlling the amount of \chem{O_3} in the lower troposphere
\citep{Kim-Newchurch1996}.
Analysis shows that the centers of high
velocity potential are associated with convergent inflow winds. The
convergence in the upper troposphere corresponds to divergence in the
lower troposphere, which is associated with downward vertical motion
at the mid-troposphere. On the opposite side, a~low velocity results
in a~vertical upward motion in the troposphere.  Centers of lower
tropospheric convergence and upper tropospheric divergence over the
equatorial western Pacific are characterized by strong upward velocity
in the mid-troposphere with maximum upward velocity outside the
equatorial regions. The equatorial eastern Pacific is associated with
upper tropospheric convergence, lower tropospheric divergence, and
mid-tropospheric downward vertical motion \citep{Wang2005}. These are consistent with
Pacific climate features of the western Pacific warm pool and the
equatorial eastern Pacific cold tongue.


\subsection{Zonal mean tropospheric \chem{O_3} changes}

\chem{O_3} trends are caused by long-term changes in chemistry
or dynamics or both.  Zonal mean time series of monthly averaged
tropospheric \chem{O_3} over four latitude bands (50--30{\degree}\,S,
20{\degree}\,S--0, 0--20{\degree}\,N and 30--50{\degree}\,N) are
displayed in Figs.~\ref{trend30s_50s}--\ref{trend0_20n} with the time
labeling on the $x$~axis corresponding to the first month of the year.
The TOC time series (black) are overlaid with their fitting curves
(blue) and linear term plus offset (hereinafter referred to as linear term,
(violet)) from the regression model. The fit residuals are shown below
the TOC time series. The individual terms from the regression
including the harmonics, QBO, linear and ENSO terms are shown in the
panels below, respectively. The time series of the fit parameters are
coloured differently, i.e., pink, green, orange, and brown
representing the harmonic, QBO, linear and ENSO terms, respectively,
while the overlaid time series in black represents the time series
with all fit terms removed except the particular fit parameter.

Good fit quality is obtained in all four latitude bands. The fit residuals
are typically on the order of a~few DU.  The annual cycles make
comparable contributions to the fitting curves in all four latitude
bands.
The harmonic terms show high values during hemispheric spring and
summer in Figs.~\ref{trend30s_50s} and \ref{trend30n_50n}, which
represent 50--30{\degree}\,S and 30--50{\degree}\,N latitude
bands, respectively.  Figures \ref{trend20s_0} and \ref{trend0_20n}
show TOC changes in the latitude bands 20{\degree}\,S--0 and
0--20{\degree}\,N, respectively. We separated the southern tropics
from the northern tropics due to the seasonal shift in TOC
distributions in the tropics also reported in other studies
\citep[e.g.,][]{Valks2014, Ziemke2014}. In Fig.~\ref{trend20s_0}, the
harmonic terms show high values mainly during austral spring/summer.
The \chem{O_3} maxima observed in Fig.~\ref{trend20s_0} during austral
spring/summer can be attributed mainly to transport of pollution and
photochemical production of \chem{O_3}. The combination of emissions
from extensive biomass burning and transport of upper tropospheric
\chem{O_3} production from lightning \chem{NO}$_x$, especially in the
southern tropics can contribute to the observed \chem{O_3} maxima in
the top panel of Fig.~\ref{trend20s_0}. Such tropospheric \chem{O_3}
maxima have been observed during austral spring/summer in several studies
\citep[e.g.,][]{Sauvage2007,Randel-Thompson2011,Thompson2014,
  Ziemke2014, Ebojie2014,Ebojies2014}.  The QBO fit captures the interannual variability in
the regression analysis better in Fig.~\ref{trend20s_0} than in
Fig.~\ref{trend0_20n}, while the reverse is the case for the ENSO
fit. The tropospheric \chem{O_3} seasonal cycle in the tropical region
is primarily governed by two processes: the seasonal migration of the
ITCZ and the photochemical production of \chem{O_3} from biomass
burning and lightning activity \citep{Moxim2000,Thompson2000}.  These
processes lead to atmospheric layers with strongly enhanced \chem{O_3}
volume mixing ratios and hence the TOCs.  The \chem{O_3} maxima
observed in the subtropics and midlatitudes of both hemispheres (top
panels of Figs.~\ref{trend30s_50s} and \ref{trend30n_50n}) can be
associated with the combination of transport of \chem{O_3} and its
precursors, local or regional photochemical production of \chem{O_3}
as well as stratospheric intrusion of \chem{O_3} rich air masses into
the troposphere \citep{Chandra2004,Stohl2007,Pfister2008}.  The
intercontinental transport of \chem{O_3} and its precursors can also
contribute to the observed changes in tropospheric \chem{O_3}
\citep{Wild2001,Stohl2007,Cooper2012}.  In the southern subtropics and
midlatitudes, the influence of both stratospheric intrusion and
emissions from biomass burning \citep[e.g.,][]{Sauvage2006,Wai2014}
play a~key role in the observed \chem{O_3} maxima during austral
spring in Fig.~\ref{trend30s_50s}.  The QBO fit does not capture
a~significant contribution of the interannual variability in the
regression analysis as observed in Figs.~\ref{trend30s_50s} and
\ref{trend30n_50n}.  The possible ENSO contributions are better
identified in Fig.~\ref{trend30n_50n} than in Fig.~\ref{trend30s_50s}.

Table~\ref{zone} shows the results of zonal means TOC trends, the mean values and
standard deviation. An insignificant increase in
tropospheric \chem{O_3} of $0.1 \pm 0.2$\,{{\%}}\,\unit{yr^{-1}} with
a~mean value of $30.4 \pm 4.9$ DU is observed in the
50--30{\degree}\,S latitude band (Fig.~\ref{trend30s_50s}). This
region is mainly over the ocean with less influence from land, hence
there is uniformity in \chem{O_3} distributions in this zonal band as
observed in \citet{Ebojies2014} and in Figure 12 of \citet{Ebojie2014}.  The observed
insignificant positive change in \chem{O_3} in this zonal band may be
due to a~larger contribution of the significant positive change in TOC
over southern South America compared to the significant negative
change or no trend over the oceans as shown in
Fig.~\ref{global_trnd_5_5}.

A~statistically insignificant decrease in TOCs of $-0.2 \pm
0.4$\,{{\%}}\,\unit{yr^{-1}} with a~mean value of $39.0 \pm 3.9$\,DU
is observed between 30{\degree}\,N--50{\degree}\,N
(Fig.~\ref{trend30n_50n}). This zonal band is greatly influenced by
land-sea contrast in tropospheric \chem{O_3} and anthropogenic
activity. The observed insignificant decrease in TOC over this
latitude band could be associated with stronger significant negative
TOC changes over Europe and North America than the significant
positive change over northern China as observed in
Fig.~\ref{global_trnd_5_5}. Similar negative trends in TOC over Europe and North America have been
observed in other studies \citep[e.g.,][]{Parrish2012,Vestreng2009,Cooper2012,Cooper2014}.

In the 20{\degree}\,S--0 latitudes band (Fig.~\ref{trend20s_0}), an
insignificant positive change in tropospheric \chem{O_3} of $0.3 \pm
0.4$\,{{\%}}\,\unit{yr^{-1}} with a~mean value of $27.5 \pm 3.9$\,DU
is observed. The insignificant positive change in ozone in this region
may be a~consequence of a~larger contribution by significant positive
change in TOC over southern Africa and northern Australia outflow
compared to the small significant negative change in TOC or no trend
over the Oceans. The significant positive changes observed over
southern Africa and the North Australian outflow region as shown in
Fig.~\ref{global_trnd_5_5} may be a~result of changes in \chem{O_3}
precursor emission from anthropogenic activities. Changes in
atmospheric circulation and emissions from biomass burning may also
have contributed to the observed positive change in \chem{O_3} in this
latitude band \citep{Randel-Thompson2011,Thompson2014}. In the
0--20{\degree}\,N latitude band corresponding to Fig.~\ref{trend0_20n}, an
insignificant change in tropospheric \chem{O_3} of $0.1 \pm
0.5$\,{{\%}}\,\unit{yr^{-1}} with a~mean value of $29.3 \pm 2.7$\,DU
is observed.
The small positive trend in TOC over this region results from many small positive and negative trends,
most of which are statistically insignificant as observed in Fig.~\ref{global_trnd_5_5}.


\subsection{Quasi-biennial oscillation and El Ni\~{n}o-Southern Oscillation response to changes in tropospheric ozone column}
The contributions of the proxies used in the regression analysis were tested to show their response to changes in tropospheric \chem{O_3}.
Figures \ref{global_enso} and \ref{global_qbo} show ENSO and QBO responses to changes in TOC for the period 2003 – 2011, respectively. They are derived by subtracting the minimum 
from the maximum proxy response in the time series for each grid box. The regions marked with crosses represent fitting coefficients of the proxy or in the case of QBO, at least where one of the coefficients 
is statistically significant at the 95 \,{{\%}} confidence level. In Fig.\ref{global_enso}, extensive regions of significant ENSO responses in TOC are observed. These may be the response of 
tropospheric circulations and/or troposphere--stratosphere--troposphere events connected with ENSO changes \citep[e.g.,][]{Reichler2005, Graf_Zanchettin2012, Butler2014}, which occur mainly at the
middle and high latitudes, particularly in the NH. Significant ENSO signals are observed over the equatorial region extending towards higher latitudes mainly in the NH.
Significant ENSO signals have been observed at some tropical and extratropical regions in previous studies \citep[e.g.,][]{Trenberth1998, Hoerling1997, Neu2014, Oman2013, Trenberth2002, Ziemke2015,Graf_Zanchettin2012,Scaife2014, Butler2014}, although with different amplitudes. 
The observed ENSO signals may be due to large-scale atmospheric circulation teleconnection
patterns excited by tropical atmospheric heating anomalies  \citep[e.g.,][]{Horel_Wallace1981,Trenberth1998, Trenberth2002}, which involve both
a tropospheric and a stratospheric pathway. While ENSO’s climate impacts over North America are largely associated with a tropospheric pathway, its climate impacts
over the North Atlantic and Eurasia are greatly affected by a stratospheric pathway \citep[e.g.,][]{Graf_Zanchettin2012, Butler2014}. The tropospheric pathway
involve poleward propagating Rossby waves generated by anomalous atmospheric heating in equatorial latitudes, which may be trapped by the subtropical jet and
propagate eastward within the subtropical waveguide \citep[e.g.,][]{Trenberth1998,Trenberth2002, Graf_Zanchettin2012}. Changes in the midlatitude
storm tracks during El Ni\~{n}o events can alter the vorticity flux by transient synoptic eddies, thus modulating the amplitude and structure of the extratropical
planetary-scale wave trains initiated by tropical sea surface temperature anomalies. The stratospheric pathway consists of planetary waves that propagate
primarily upwards, greatly perturbing the stratosphere and occasionally producing extreme events such as the sudden stratospheric warmings, which subsequently
affect surface conditions below, primarily in the North Atlantic region and Eurasia \citep[e.g.,][]{Bronnimann2004, Bell2009, Cagnazzo_Manzini2009, Ineson_Scaife2009, Butler2014}.
In Fig. \ref{global_qbo}, significant QBO signals are observed in some regions at the tropics and high latitudes. \citet{Neu2014}, observed the influence of QBO on TOC changes in the midlatitudes of the NH. The significant QBO signals at the high latitudes, particularly the northern high latitudes could
be a result of the strengthening of the Polar vortex, which can induce a positive trend in the North Atlantic Oscillations, mostly during boreal winter \citep[e.g.,][]{Thompson2001, Scaife2014}. The observed QBO signal may also be due to several mechanisms including anomalies in convection \citep[e.g.,][]{Collimore2003}, the meridional circulation that maintains thermal wind balance with QBO equatorial stratospheric wind anomalies, which interacts with the troposphere or due to tropospheric eddies \citep[e.g.,][]{Randel1999,Garfinkel2011}. similar feature between QBO and ENSO signals can be observed over some regions of the southern Pacific, southern Atlantic, southern Indian Ocean, Siberia, east Asia, North America and the western Pacific mostly around Indonesia,  with ENSO signal  being higher in all the regions than the QBO signals.
Fig. \ref{plot_region_proxy} is extracted from one of the regions in the NH of Fig. \ref{global_enso}, which is denoted by A, where high values of the coefficient of ENSO and QBO signals are observed. 
In this region a maximum response of ENSO of 4.7 DU and a minimum of about  -5.2 DU (maximum difference = 9.9 DU) are observed
while maximum and minimum response of QBO of 2.6 DU and -5.2 DU (Maximum difference = 7.8 DU), respectively are observed. The ENSO fit captures
the interannual variability in the regression analysis better than the QBO fit. Also observed at the top panel of Fig. \ref{plot_region_proxy} is a significant negative change
in TOC of $-1.4 \pm 0.4$\,{{\%}}\,\unit{yr^{-1}}, which is also shown in \ref{global_trnd_5_5}. 


\subsection{Global and regional mean tropospheric \chem{O_3} changes}
\subsubsection{Changes in SCIAMACHY global tropospheric \chem{O_3} columns}

The changes in tropospheric \chem{O_3} are complex and vary both
regionally and globally. They are strongly influenced by changes in
precursor emissions, temperature, humidity, convection, STE, and
hemispheric transport patterns.  Figures \ref{global_trnd_5_5} and
\ref{global_tren_du_5_5} show global changes in SCIAMACHY TOC derived from
2003--2011 for 5{\degree} latitude\,$\times$\,5{\degree} longitude
bins in {{\%}}\,\unit{yr^{-1}} and DU\,\unit{yr^{-1}},
respectively. The colour ranges from blue to red, representing
negative to positive changes in TOCs from 2003--2011. The white
coloured area represents places with no TOC data due to the applied
data screening criteria described in \citet{Ebojie2014} and \citet{Ebojies2014}. In the
determination of TOC from SCIAMACHY LNM observations \citep{Ebojie2014,Ebojies2014}, limb scenes that are
contaminated with clouds and nadir measurements that have a~cloud
fraction of more than 0.1 were screened out. This
makes it unlikely, although not impossible, that the derived trends
may be significantly affected by possible changes in cloud parameters
from 2003 to 2011.  The errors of the derived changes in TOCs are
estimated at 1-sigma expressed in units of {{\%}}\,\unit{yr^{-1}} and
DU\,\unit{yr^{-1}}.  The regions marked with cross signs represent
changes in tropospheric \chem{O_3} significant at the 95\,{{\%}}
confidence level.


Changes in TOC are observed along the possible outflow from continents to
oceanic regions.  Statistically significant changes in tropospheric
\chem{O_3} are observed over the regions of North America, Europe,
South Asia, South America and Africa. Significant changes
in \chem{O_3} are also observed over the clean and pristine marine region including the
Pacific Ocean, Indian Ocean and the Atlantic Ocean.
The observed TOC changes might have been influenced by changes in transport of pollution, anthropogenic \chem{O_3} precursor emissions and dynamical phenomena including ENSO, the North Atlantic Oscillation/Northern Annular Mode (NAO/NAM), Southern Annular Mode (SAM) in the extra-tropics, Pacific North American (PNA), Pacific Decadal Oscillation (PDO), Atlantic Multi-Decadal Oscillation (AMO), etc. \citep[e.g.,][]{Handorf_Dethloff2009,Lin2014,Ziemke2015}. The climate patterns associated with the modes may exhibit, structural, spatial, index or no change \citep[e.g.,][]{Compo_Sardeshmukh2010, Bulic_Kucharski2012}. During El Ni\~{n}o events, the ITCZ is farther south and closer to the equator, while during La Ni\~{n}a, it is farther north and away from the equator \citep[e.g.,][]{Hastenrath1977,Schneider2014}. These alter the strength of the tropical cyclones over the Pacific and Indian Oceans, thus influencing TOC changes.
Close to the branches of the ITCZ in the Indian and Pacific ocean, and possibly close to the ITCZ in other regions, we observe significant linear trends for the analysis period 2003 to 2011. Generally air masses are different on the different sides of the ITCZ. Thus, they  have different amounts of tropospheric \chem{O_3}. Our explanation of the observed linear trends is that the positions of the Oceanic branches of the ITCZ, and perhaps also the ITCZ,  have been changing \citep[e.g.,][]{Hastenrath1977,Pike1971,Xie_Philander1994, Moxim2000,Schneider2014}. This may be induced from oscillations of the position of the ITCZ, produced by natural phenomena, or possibly an early warning of anthropogenic induced changes \citep[e.g.,][]{Hastenrath1977,Xie_Tanimoto1998,Xie_Philander1994,Chang1997,Schneider2014}. In general, changes of the position of the ITCZ, in addition to that simply expected seasonally, their origins and consequences for the production and removal of tropospheric \chem{O_3} and other pollutants require careful assessment in the future.
Longer time series of data and more research are required to identify unambiguously the origin of these trends.
Significant positive changes in \chem{O_3} are observed over North America, especially around Alaska (region~A in Fig.~\ref{global_trnd_5_5}, up to 2\,\%\,yr$^{-1}$),
which may be attributed to changes in both transport of pollution and photochemical production of \chem{O_3} from natural and anthropogenic precursor emissions. Significant
negative TOC changes (up to $-3$\,{{\%}}\,\unit{yr^{-1}}) are observed
over some parts of North America, South America and Europe (regions B,
C, D and E in Figs.~\ref{global_trnd_5_5} and
\ref{global_tren_du_5_5}), which can be attributed to decrease in
anthropogenic \chem{NO}$_x$ and VOCs emissions as well as reduction in
other tropospheric \chem{O_3} precursors \citep[e.g.,][]{Vestreng2009,
  Logan2012}. Increase in anthropogenic and biogenic halogens may also
play a~role in the observed significant negative change in TOC over
North America and Europe \citep{vonGlasow2002,Read2008}. Similar
significant negative trends in tropospheric \chem{O_3} have been
observed in some earlier studies \citep[e.g.,][]{Parrish2012,Parrish2014}.
Significant negative TOC changes (up to $-$2\,{{\%}}\,\unit{yr^{-1}})
are also observed over the marine regions of the Atlantic Ocean, the
Pacific Ocean and the Indian Ocean. The significant negative TOC
changes over these regions can be associated with an increased
photochemical sink (\chem{HO}$_x$ chemistry and photolysis of \chem{O_3}
to singlet D oxygen atoms ($^1$D)) and dilution processes due to meteorological changes
\citep{Kley1997, Wai2014}. The declining trend of tropospheric
\chem{O_3} over North America and Europe has also contributed to the
negative TOC changes over the Oceans, mostly over the outflow
regions. Changes in the amount of organic and inorganic halogens may
also play a~role in the observed negative TOC changes over the Oceanic
regions \citep{Dickerson1999,Read2008}.  Significant positive changes
in tropospheric \chem{O_3} are observed over some regions of South
America (region F in Fig.~\ref{global_trnd_5_5}, up to
2\,{{\%}}\,\unit{yr^{-1}}), South Asia (region H in
Fig.~\ref{global_trnd_5_5}, 1--3\,{{\%}}\,\unit{yr^{-1}}), part of
Africa (region G in Fig.~\ref{global_trnd_5_5}) and over the marine
regions (up to 2\,{{\%}}\,\unit{yr^{-1}}). The observed significant
positive changes in tropospheric \chem{O_3} over South America, South
Asia and part of Africa can be attributed to increase in population,
industrialization and energy consumption
\citep[e.g.,][]{Beig2007, Cooper2014}. Changes in meteorology and
intercontinental transport of pollution can also contribute to the
observed changes in \chem{O_3} over the regions of South America, South
Asia and Africa \citep[e.g.,][]{Lelieveld2004, Beig2007,Parrish2012}. The significant
change in \chem{O_3} observed over the regions of the Southern
Atlantic and southern South America can be attributed to changes in
transport patterns, anthropogenic \chem{NO}$_x$ and other tropospheric
\chem{O_3} precursors \citep[e.g.,][]{Schultz1999,Wai2014}.  In
southern Africa, the widespread biomass burning events in Angola,
Zambia and the Democratic Republic of Congo, particularly during
July--September lead to the formation of \chem{O_3} precursors. The
transport of \chem{O_3} and its precursors by the prevailing wind
systems over these regions leads to significant change in TOC
\citep{Sauvage2005,Sauvage2007a}. For instance, the persistent
low-level easterlies/southeasterlies in these regions facilitate the
westward transport of tropospheric \chem{O_3} and its precursors (see,
Fig.~\ref{season_wd}). The prevailing descending airmasses and
relatively low wind speeds over southern Africa, South America, the
Atlantic Ocean and the Indian Ocean limit the dispersion of
tropospheric \chem{O_3} and its precursors produced from burning
plumes in Africa and South America. The prevailing high-pressure
system over southern Africa leads to the recirculation of plumes of
tropospheric \chem{O_3} and its precursors over the continent before
exiting from the southern or western part of the continent to the
Indian Ocean or the Atlantic Ocean south of the ITCZ, respectively
\citep[e.g.,][]{Edwards2006,Wai2014}.

Biomass burning activities in South America, which peak from
August--October, lead to formation of tropospheric \chem{O_3} and
its precursors. \chem{O_3} and its precursors can
remain in the planetary boundary layer before they are transported to
the upper troposphere via deep convective processes
\citep[e.g.,][]{Wu2011,Sauvage2006}. In the free troposphere, the
\chem{O_3} plumes can be effectively transported by westerlies through
the continental outflows located southeast of South America
(see, Fig.~\ref{season_wd}).  The significant increase in \chem{O_3}
(up to 2\,{{\%}}\,\unit{yr^{-1}}) observed over the North Australia
outflow (region I) can be associated with changes in the wind pattern.
The significant increase in TOC over south Asia can mainly be
associated with changes in anthropogenically produced \chem{O_3}
precursors from both domestic and industrial combustion as well as
biomass burning \citep{Liu2002,Parrish2012}. The \chem{O_3} produced
over these regions is transported towards the western Pacific Ocean by
large-scale dynamics controlled by the prevailing wind systems. This
can contribute to changes in TOC over some regions of the
western Pacific Ocean. The variation in TOC changes observed over the
southern high latitudes may be a~consequence of gaps in the TOC time
series due to the applied data screening criteria \citep{Ebojie2014,Ebojies2014}.

The uncertainties of the fit parameters are determined from the
covariance matrix of the regression.
The uncertainty in SCIAMACHY TOC has been discussed in detail by \citet{Ebojie2014} and \citet{Ebojies2014}.
In addition to the regression uncertainties, trend uncertainties due to instrumental artefacts, e.g. scan mirror degradation, introducing potential drifts in the data may also play a role but the effect were found to be small
\citep{Bramstedt2009,Ebojie2014,Ebojies2014}. Inspection of the the colour map of
the {{\%}}\,\unit{yr^{-1}} and DU\,\unit{yr^{-1}} trend uncertainties
(figures not shown) shows that the uncertainty in \chem{O_3} trend is
generally below 1\,{{\%}}\,\unit{yr^{-1}} and 0.5\,DU\,\unit{yr^{-1}}
globally, respectively \citep{Ebojies2014}. The uncertainties in TOC changes become high
at the Southern high latitudes probably due to gaps in data coverage
associated with prevailing cloud cover and the variability of
tropospheric ozone and its precursors in the region.

\subsubsection{Regional mean tropospheric \chem{O_3} changes}

Investigation of changes in the TOCs for some specific regions
(Fig.~\ref{plot_region}), which are highlighted in
Figs.~\ref{global_trnd_5_5} and \ref{global_tren_du_5_5}, shows
changes in TOCs over the years 2003--2011, which could be due to
variations in meteorology and anthropogenic emissions of ozone
precursors.  In Table~\ref{region} and Fig.~\ref{plot_region}, five
out of the eleven regions extracted from Fig.~\ref{global_trnd_5_5} show
significant increase, two regions show significant decrease while the
remaining four regions show insignificant change in TOCs.  The changes
in TOCs over these regions could be attributed to variation in
tropospheric background \chem{O_3}, temporal variation of precursor
emissions, systematic changes in transport meteorology as well as by
the changes in photochemical oxidation and removal processes. Most of
the regions show a~pronounced seasonal cycle with maximum TOC values
during hemispheric spring and summer with different amplitudes.
A~significant positive change and variability in the mean tropospheric
\chem{O_3} is observed over Alaska (region A). The significant positive change over this region could be a result of
changes in precursors emissions from biomass burning \citep{Stohl2007,Oltmans2010}. Changes in transport of \chem{O_3} and its precursors from
the stratosphere or lower latitudes as well as the influence of rapid climate change may also play a role \citep{Oltmans2013}.
The variability in TOC
over this region is high, which could probably be a~result of the gaps
in the TOC time series.
TOC minimum is observed during 2010 winter
over region B, which corresponds to Eastern North American
outflow. Negative change in TOC is observed over the region, which can
be associated to the declining trend in TOC over US as
reported in earlier studies \citep{Cooper2012,Cooper2014,Parrish2012}.
The seasonal variation observed over regions C (Southern Europe)
becomes less pronounced between 2007 and 2009. The TOC change over the
region is negative, which could probably be due to reduction of
\chem{O_3} precursor emissions.  The region over Siberia (region D)
shows significant negative change in TOC, which could probably be
a~result of response to emission reduction in \chem{O_3} precursor and
change in anthropogenic and biogenic halogens that are effective in
\chem{O_3} destruction \citep{Parrish2012}.  In general, the observed
significant negative change in TOC over the northern midlatitude,
particularly around Europe and North America (regions B, C and D) is
a~combination of changes in dynamics and chemical processes. The
chemical processes involve the reduction in \chem{O_3} precursor
emissions and increase in anthropogenic and biogenic halogens that are
effective in \chem{O_3} destruction \citep{Vestreng2009, Parrish2012}.
Synoptic-scale meteorological variability and dynamical modes
including changes in tropospheric circulation indices such as those of
the Arctic Oscillation (AO) and the NAO can
play a~role \citep{Zhou2001,Chipperfield2003}. A~trend toward more
equatorward planetary wave fluxes may also play a~role in the negative
change in tropospheric \chem{O_3} in the northern midlatitude
\citep{Kuroda_Kodera1999, Chen2005}. Another important process that
may be contributing to the decrease in tropospheric \chem{O_3} in the
northern midlatitude is the decrease in the average total wave flux
entering the stratosphere \citep{Newman_Nash2000,Reid2000}.
Pronounced seasonal variation in TOC is observed over the regions of
northern South America, southern South America, southern Africa,
southeast Asia and North Australia outflow (Regions E--I), with each region
exhibiting significant change in TOC. Strong seasonal cycles in TOC are also observed
over Galapagos Island (region J) and the Southern Pacific Ocean (region K),
with both regions showing insignificant negative change in TOC.
The significant negative change
in TOC observed over northern South America (region E) could be due to
changes in humidity and solar actinic flux \citep{Revell2015}. The
significant positive change in TOC observed over regions F and G
(southern South America and central Africa) is primarily due to changes in
pollution emission from biomass burning and anthropogenic activities
\citep[e.g.,][]{Fishman1991}. The significant positive change in TOC
over southeast Asia (region H) could be associated to changes in
anthropogenic emissions as well as changes in transport pattern as
observed by many studies
\citep[e.g.,][]{Beig2007,Parrish2012}. Changes in population and
increased industrialization in east Asia have led to great changes in
anthropogenic precursor emissions in this region. The observed
significant positive TOC change over northern China (i.e., part of
region H) may be associated with high \chem{O_3} over the northwestern
mountainous areas during summer and in the afternoon hours
\citep{Tang2012}.  Over the northern Australia outflow (region I),
a~positive significant change in TOC is observed, which could probably
be a~result of changes in transport patterns and precursor emissions
\citep{Wai2014}.  Over the Galapagos Islands (region J), which are among the most renowned natural sites in the world with less impact of anthropogenic activities, an insignificant negative change in TOC is observed. The observed insignificant negative change in TOC in this region could be related to synoptic-scale meteorological variability. Over the Southern Pacific Ocean (region K), insignificant negative change in TOC is observed, which could be associated with the influence of photochemical reactions involving hydroxyl (OH) and hydroperoxy (\chem{HO_2}) radicals and changes in meteorology.
A~summary of the changes in TOCs as well as the mean
values and standard deviations over the entire time series for the
different regions are summarised in Table~\ref{region}.

\conclusions [Summary and conclusions]
\label{conclusions}

In this study, changes in tropospheric \chem{O_3} columns (TOCs) were
derived from SCIAMACHY limb-nadir-matching (LNM) observations during
the period 2003--2011.  TOC shows a~statistically insignificant change at the 95\,{{\%}} confidence
level of $-0.2 \pm 0.4$, $0.3 \pm 0.4$, $0.1 \pm 0.5$
and $0.1 \pm 0.2$\,{{\%}}\,\unit{yr^{-1}} in the latitude bands
30--50{\degree}\,N, 20{\degree}\,S--0, 0--20{\degree}\,N and
50--30{\degree}\,S, respectively.  From the global analysis of changes
in TOC, positive changes significant at the 95\,{{\%}} confidence
level were observed over South Asia (1--3\,{{\%}}\,\unit{yr^{-1}}),
the South American continent (up to 2\,{{\%}}\,\unit{yr^{-1}}), North
America, especially around Alaska (1--3\,{{\%}}\,\unit{yr^{-1}}), the
Australian outflow region (up to 2\,{{\%}}\,\unit{yr^{-1}}) as well as
over some regions of the African continent (up to
2\,{{\%}}\,\unit{yr^{-1}}). The positive change in \chem{O_3} over
these regions may be attributed to changes in emissions of nitrogen
oxides (\chem{NO}$_x$) and other \chem{O_3} precursors. In addition,
changes in meteorology and intercontinental transport of \chem{O_3}
and its precursors might also have played a~role in the observed
changes in \chem{O_3} over the regions.  Over the marine regions,
significant positive TOC changes were derived over the Indian Ocean
(1--2\,{{\%}}\,\unit{yr^{-1}}) and the southern Atlantic Ocean (up to
2\,{{\%}}\,\unit{yr^{-1}}).  The observed positive change in TOC over
the Oceanic regions may be associated with changes in dynamical
processes such as advection, local emissions, entrainment and
transport of \chem{O_3} and its precursors from the source regions.
Significant negative TOC trends were observed over some regions of
Europe and the North American continent (up to
$-$3\,{{\%}}\,\unit{yr^{-1}}), which may be attributed to reduction
in \chem{NO}$_x$ and other tropospheric \chem{O_3} precursors
emissions over the regions. Halogen chemistry might have also
contributed to the observed changes in tropospheric \chem{O_3}. Over
the Oceanic regions including the Pacific, Atlantic and Indian Oceans,
significant decrease in TOC ($-$1 to $-$3\,{{\%}}\,\unit{yr^{-1}}) was
observed.  The observed negative change in tropospheric \chem{O_3}
over these regions may be associated with the influence of
photochemical reactions involving hydroxyl (OH) and hydroperoxy
(\chem{HO_2}) radicals. Furthermore, changes in the amount of organic
and inorganic halogens as well as changes in Dissolved Organic Matter
(DOM) photochemistry in surface waters could be an additional source
of volatile organic compounds that have contributed to the destruction
of \chem{O_3}.  In general, changes in TOC in the observed period are
smaller when compared with results obtained from studies on tropospheric
\chem{O_3} trends in the previous years
\citep[e.g.,][]{Marenco1994,Simmonds2004} probably due to the slowdown
in the growth rate of \chem{O_3} precursors as well as changes in the
lowermost stratospheric \chem{O_3} \citep{WMO2011}.  The strengthening
of legislation on air pollution related policies have contributed to
the reduction of tropospheric \chem{O_3} amounts and its precursor
emissions. Changes in the tropopause altitude might have also contributed to the zonal or global change in tropospheric \chem{O_3}.
Investigation of the response of El Ni\~{n}o-Southern Oscillation and quasi-biennial oscillation to changes in TOC for the period 2003--2011 shows extensive regions of significant ENSO responses to changes in TOC and significant QBO response to changes in TOC over some regions.

It should be noted that a final attribution of the variability and trends observed in specific regions to the different underlying processes is not possible based on the analysis alone as presented in this paper. This task requires in addition dedicated model simulations that will allow us to separate the different relevant processes. The value of the study lies more in the presentation of experimental results on global and regional tropospheric ozone columns to be used as a benchmark for model simulations.
In conclusion it can be stated that the results obtained in this paper provide first hand information on
tropospheric \chem{O_3} changes both zonally and globally.

\begin{acknowledgements}
  We thank ESA and German Aerospace DLR for providing SCIAMACHY level
  1 data for this study. This work was funded in parts by the German
  Aerospace DLR project SADOS (FKZ 50EE1105), by ESA through the
  SCIAMACHY Quality Working Group and Ozone$\_$cci, by Ernst-Moritz-Arndt-University of
  Greifswald and the University and State of Bremen,
  Germany. The SCIAMACHY instrument, which flew on ESA's Envisat
  spacecraft, was supported by Germany, the Netherlands and Belgium.
  {\newline}{\newline}
  The article processing charges for this open-access publication {\newline} were covered by the University of Bremen.
\end{acknowledgements}

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{Ziemke},~J.~R., {Olsen},~M.~A., {Witte},~J.~C., {Douglass},~A.~R., {Strahan},~S.~E., {Wargan},~K., {Liu},~X., {Schoeberl},~M.~R., {Yang},~K., {Kaplan},~T.~B., {Pawson},~S., {Duncan},~B.~N., {Newman},~P.~A., {Bhartia},~P.~K., and {Heney},~M.~K.: {Assessment and applications of NASA ozone data products derived from Aura OMI/MLS satellite measurements in context of the GMI chemical transport model},~J. Geophys. Res.-Atmos., 119, 5671--5699, doi:\href{http://dx.doi.org/10.1002/2013JD020914}{10.1002/2013JD020914}, 2014.

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}
\end{thebibliography}

\begin{table}
\caption{Trend analysis results for SCIAMACHY tropospheric \chem{O_3} for different latitude ranges. Also included are mean values and standard deviation over the entire time series.}
\begin{tabular}{lll}
\tophline
Lat. range & Mean value and SD (DU) & Trend ({\%}\,\unit{yr^{-1}})\\
\middlehline
70--50{\degree}\,S & $24.8 \pm 3.7$  &  $0.0 \pm 0.3$ \\
50--30{\degree}\,S & $30.4 \pm 4.9$  &  $0.1 \pm 0.2$ \\
30--10{\degree}\,S & $31.1 \pm 4.7$  &  $0.3 \pm 0.3$ \\
10{\degree}\,S--10{\degree}\,N & $26.1 \pm 3.1$  &  $0.1 \pm 0.5$ \\
20{\degree}\,S--0           & $27.5 \pm 3.9$  &  $0.3 \pm 0.4$ \\
0--20{\degree}\,N           & $29.3 \pm 2.7$  &  $0.1 \pm 0.5$ \\
10--30{\degree}\,N & $33.9 \pm 2.6$  &  $0.2 \pm 0.5$ \\
30--50{\degree}\,N & $39.0 \pm 3.9$  & $-0.2 \pm 0.4$ \\
50--70{\degree}\,N & $35.9 \pm 6.3$  & $-0.4 \pm 0.4$ \\
\bottomhline
\end{tabular}
\label{zone}
\end{table}

\begin{table}
\caption{Trend analysis results for SCIAMACHY tropospheric \chem{O_3} for different regions. Also included are mean values and standard deviation over the entire time series. Bold text indicates statistically significant changes in TOC.}\begin{tabular}{lllll}
\tophline
Region & Lat. range &  Long. range & Mean value and & Trend ({\%}\,\unit{yr^{-1}})\\
&&& SD (DU)\\
\middlehline
A. Alaska & \textbf{60--70{\degree}\,N}   & \textbf{165--130{\degree}\,W} &  $\boldsymbol{36.7 \pm 11.0}$ &  $\boldsymbol{1.4 \pm 0.6}$\\
B. North American outflow & 25--45{\degree}\,N   & 85--70{\degree}\,W   & $40.3 \pm 4.7$   & $-0.8 \pm 0.4$ \\
C. Southern Europe & 35--50{\degree}\,N   & 1--25{\degree}\,E   & $41.6 \pm 4.3$   & $-0.9 \pm 0.5$ \\
D. Siberia  & \textbf{50--65{\degree}\,N}   & \textbf{50--80{\degree}\,E}   & $\boldsymbol{39.3 \pm 6.5}$  & $\boldsymbol{-1.8 \pm 0.5}$\\
E. Northern South America  & \textbf{10--0{\degree}\,S}    & \textbf{80--65{\degree}\,W}   & $\boldsymbol{25.4 \pm 4.9}$  & $\boldsymbol{-1.6 \pm 0.5}$\\
F. Southern South America & \textbf{40--20{\degree}\,S}   &  \textbf{75--45{\degree}\,W}   & $\boldsymbol{28.6 \pm 4.7}$  &  $\boldsymbol{1.2 \pm 0.3}$\\
G. Southern Africa & \textbf{15--5{\degree}\,S}    & \textbf{25--35{\degree}\,E}  & $\boldsymbol{27.6 \pm 5.3}$  &  $\boldsymbol{1.6 \pm 0.5}$\\
H. Southeast  Asia & \textbf{15--35{\degree}\,N}   & \textbf{80--115{\degree}\,E}  & $\boldsymbol{34.6 \pm 5.1}$  &  $\boldsymbol{1.9 \pm 0.5}$\\
I. North Australia outflow & \textbf{20--10{\degree}\,S}   & \textbf{100--130{\degree}\,E}  & $\boldsymbol{26.8 \pm 4.4}$  &  $\boldsymbol{1.2 \pm 0.4}$\\
J. Galapagos Islands & 10{\degree}\,S--5{\degree}\,N   & 105--85{\degree}\,W  & $27.7 \pm 5.2$  &  $-0.7 \pm 0.5$\\
I. Southern Pacific Ocean & 70--30{\degree}\,S   & 120--75{\degree}\,W  & $25.4 \pm 3.7$  &  $-0.4 \pm 0.3$\\
\bottomhline
\end{tabular}
\label{region}
\end{table}

\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f01.png}
 \caption{Global seasonal distribution of tropospheric \chem{O_3}
   in DU from 2003--2011 for 5{\degree} latitude $\times$ 5{\degree}
   longitude bins. For latitudes above about 60{\degree} in each
   hemisphere \chem{O_3} columns are only observed during summer.}
\label{season_oz}
\end{figure}

\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f02.png}
\caption{Global seasonal distribution of the 500\,\unit{hPa} winds from the ECMWF (European Centre for
  Medium-Range Weather Forecasts) Reanalysis Interim (ERA-I) wind data from 2003--2011 for 5{\degree}
  latitude\,$\times$\,10{\degree} longitude
  bins.}
\label{season_wd}
\end{figure}


\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f03.pdf}
\caption{Time series (in Dobson Units (DU)) from SCIAMACHY for
  50--30{\degree}\,S (black) with overlaid fitting curve
  (blue in top panel) and fit residuals (second panel). Below, the
  individual components of the multi-variate linear regression are shown (from top
  to bottom): harmonic (pink), QBO (light green), linear change
  (orange), and ENSO (brown). Each of these terms is overlaid by the
  time series with all other components of the fitting curve
  subtracted.}
\label{trend30s_50s}
\end{figure}

 \begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f04.pdf}
\caption{Same as Fig.~\ref{trend30s_50s} but for  30--50{\degree}\,N.}
\label{trend30n_50n}
\end{figure}

\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f05.pdf}
\caption{Same as Fig.~\ref{trend30s_50s} but for 20{\degree}\,S--0.}
\label{trend20s_0}
\end{figure}

\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f06.pdf}
\caption{Same as Fig.~\ref{trend30s_50s} but for 0--20{\degree}\,N.}
\label{trend0_20n}
\end{figure}


\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/ENSO.pdf}
\vspace*{-20mm}
\caption{Maximum ENSO response (DU) to changes in SCIAMACHY tropospheric \chem{O_3} columns derived for the period 2003 -- 2011 with
  5{\degree} latitude\,$\times$\,5{\degree} longitude resolutions. The maximum response is derived from the difference between the maximum and minimum of the ENSO term in the regression. The regions with crosses indicate the grid boxes where the ENSO regression coefficient is statistically significant to 2$\sigma$.}
\label{global_enso}
\end{figure}


\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/QBO.pdf}
\vspace*{-20mm}
\caption{Same as Figure \ref{global_enso}, but for QBO. Here the maximum response is derived from the sum of the two QBO terms in the regression. The regions with crosses indicate the grid boxes where at least one of the QBO regression coefficient is statistically significant to 2 $\sigma$.}
\label{global_qbo}
\end{figure}


\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/region_proxy_40N-65N_45E-90E.pdf}
\vspace*{-70mm}
\caption{Same as Fig.~\ref{trend30s_50s} but for the region  40--65{\degree}\,N and 45--90{\degree}\,E.}
\label{plot_region_proxy}
\end{figure}

% \begin{figure}
% \includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f07.pdf}
% \caption{Changes in SCIAMACHY tropospheric \chem{O_3} columns
%   ({{\%}}\,\unit{yr^{-1}}) derived for the period 2003--2011 with
%   5{\degree} latitude\,$\times$\,5{\degree} longitude resolution. The
%   regions with cross signs describe statistically significant change
%   and the white coloured areas represent places with no trend
%   data.}
% \label{global_trnd_5_5}
% \end{figure}

% \begin{figure}
% \includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f08.pdf}
% \caption{Changes in SCIAMACHY tropospheric \chem{O_3} columns (DU/yr)
%   derived for the period 2003--2011 with 5{\degree}
%   latitude\,$\times$\,5{\degree} longitude resolution. The regions
%   marked with crosses describe statistically significant trends and
%   the white coloured areas represent places with no trend
%   data.}
% \label{global_tren_du_5_5}
% \end{figure}


\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/acp-2015-511-discussions-f07.pdf}
\vspace*{-20mm}
\caption{Changes in SCIAMACHY tropospheric \chem{O_3} columns
  ({{\%}}\,\unit{yr^{-1}}) derived for the period 2003--2011 with
  5{\degree} latitude\,$\times$\,5{\degree} longitude resolutions. The
  regions with cross signs describe statistically significant change
  and the white coloured areas represent places with no trend
  data.}
\label{global_trnd_5_5}
\end{figure}




\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/acp-2015-511-discussions-f08.pdf}
\vspace*{-20mm}
\caption{Same as Figure \ref{global_trnd_5_5}, but for changes in units of DU/yr}
\label{global_tren_du_5_5}
\end{figure}



% \begin{figure}
% \includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f09.pdf}
% \caption{Time series (in Dobson Units (DU)) (black) from SCIAMACHY
%   (for the different regions A--I shown in Fig.~\ref{global_trnd_5_5})
%   with overlaid fitting curves (blue) and linear change plus offset
%   (violet) for the years
%   2003--2011.}
% \label{plot_region}
% \end{figure}


\begin{figure}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/acp-2015-511-discussions-f09.pdf}
\caption{Time series (in Dobson Units (DU)) (black) from SCIAMACHY
  (for the different regions A--K shown in Figures ~\ref{global_trnd_5_5} and Fig.~\ref{global_tren_du_5_5})
  with overlaid fitting curves (blue) and linear change plus offset
  (violet) for the period   2003--2011.}
\label{plot_region}
\end{figure}

\begin{figure}
\vspace*{-50mm}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/trop_O3_trend_timeseries_10S-5N_105W-85W.pdf}
\vspace*{-100mm}
\caption{Region J}
\label{plot_region1}
\end{figure}

\begin{figure}
\vspace*{-50mm}
\includegraphics[width=120mm]{acp-2015-511-discussions-figures-version3/new/trop_O3_trend_timeseries_70S-30S_120W-75W.pdf}
\vspace*{-100mm}
\caption{Region K}
\label{plot_region2}
\end{figure}

% \begin{figure*}[!ht]
% \centering{
% \vspace*{-2mm}
% \hspace*{-32mm}
% \begin{subfigure}[b]{0.28\textwidth}
% \includegraphics[width=8.2cm,bb = 101 563 557 662]{acp-2015-511-discussions-figures-version3/new/trop_O3_trend_timeseries_10S-5N_105W-85W.pdf}
%  \end{subfigure}
% \hspace*{32mm}
% \begin{subfigure}[b]{0.28\textwidth}
% \includegraphics[width=8.2cm,bb = 101 563 557 662]{acp-2015-511-discussions-figures-version3/new/trop_O3_trend_timeseries_70S-30S_120W-75W.pdf}
%  \end{subfigure}
%  \caption{Time series (in Dobson Units (DU)) (black) from SCIAMACHY
%   (for the regions J and K shown in Fig.~\ref{global_trnd_5_5})
%   with overlaid fitting curves (blue) and linear change plus offset
%   (violet) for the years
%   2003--2011.}
% \end{figure*}
% \FloatBarrier

\end{document}

\endinput

