ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-13373-2017Hemispheric asymmetry in stratospheric NO2 trendsYelaMargaritayelam@inta.esGil-OjedaManuelNavarro-ComasMónicaGonzalez-BartoloméDavidPuenteduraOlgahttps://orcid.org/0000-0002-4286-1867FunkeBerndhttps://orcid.org/0000-0003-0462-4702IglesiasJavierRodríguezSantiagoGarcíaOmairaOchoaHéctorDeferrariGuillermoAtmospheric Research and Instrumentation Branch. Instituto Nacional de
Técnica Aeroespacial (INTA), Ctra. Ajalvir s/n, Torrejón de Ardoz,
28850 Madrid, SpainInstituto de Astrofísica de Andalucía (CSIC), Granada, SpainCentro de Investigación Atmosférica de Izaña (CIAI),
Agencia Estatal de Meteorología (AEMET), SpainDirección Nacional del Antártico/Instituto Antártico
Argentino, 25 de Mayo 1143, San Martín Provincia de Buenos Aires,
ArgentinaCentro Austral de Investigaciones Científicas (CADIC), Ushuaia, ArgentinaUniversidad Nacional de Tierra del Fuego (UNTDF), Ushuaia, ArgentinaMargarita Yela (yelam@inta.es)10November20171721133731338928April201729May201718September201720September2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/13373/2017/acp-17-13373-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/13373/2017/acp-17-13373-2017.pdf
Over 20 years of stratospheric NO2 vertical column density (VCD)
data from ground-based zenith DOAS spectrometers were used for trend
analysis, specifically, via multiple linear regression. Spectrometers from
the Network for the Detection of Atmospheric Composition Change (NDACC) cover
the subtropical latitudes in the Northern Hemisphere (Izaña,
28∘ N), the southern Subantarctic (Ushuaia, 55∘ S) and
Antarctica (Marambio, 64∘ S, and Belgrano, 78∘ S). The
results show that for the period 1993–2014, a mean positive decadal trend of
+8.7 % was found in the subtropical Northern Hemisphere stations,
and negative decadal trends of -8.7 and -13.8 % were found in
the Southern Hemisphere at Ushuaia and Marambio, respectively; all trends are
statistically significant at 95 %. Belgrano only shows a significant
decadal trend of -11.3 % in the summer/autumn period. Most of the
trends result from variations after 2005. The trend in the diurnal build-up
per hour (DBU) was used to estimate the change in the rate of
N2O5 conversion to NO2 during the day. With minor
differences, the results reproduce those obtained for NO2. The
trends computed for individual months show large month-to-month variability.
At Izaña, the maximum occurs in December (+13.1 %), dropping
abruptly to lower values in the first part of the year. In the Southern
Hemisphere, the polar vortex dominates the monthly distributions of the
trends. At Marambio, the maximum occurs in mid-winter (-21 %),
whereas at the same time, the Ushuaia trend is close to its annual minimum
(-7 %). The large difference in the trends at these two relatively
close stations suggests a vortex shift towards the Atlantic/South American
area over the past few years. Finally, the hemispheric asymmetry obtained in
this work is discussed in the framework of the results obtained by previous
works that considered tracer analysis and Brewer–Dobson circulation. The
results obtained here provide evidence that the NO2 produced by
N2O decomposition is not the only cause of the observed trend in
the stratosphere and support recent publications pointing to a dynamical
redistribution starting in the past decade.
Introduction
Stratospheric nitrogen dioxide (NO2) has been a subject of interest
since the late sixties, when the use of supersonic aircraft flying at
stratospheric levels was being considered. At that time, studies on the
impact of nitrogen oxides on the ozone in the stratosphere were initiated
(Johnston, 1971). The interest in this atmospheric layer was enhanced in the
mid-seventies, powered by the discovery of the ozone depletion potential of
halogenated compounds (Stolarski and Cicerone, 1974).
Nitrogen oxides interact with ozone both directly and indirectly. Nitric
oxide (NO) reacts with ozone, forming NO2 and O2.
NO is recovered by NO2 reaction with atomic oxygen and, in
day time, by NO2 photolysis. These catalytic reactions result in
ozone reduction. On the other hand, NO2 reacts with BrO and
ClO, mitigating the ozone depletion potential of halogenated
compounds. NOx oxidation products, such as HNO3 and
N2O5, have relatively long lifetimes and therefore act as
NO2 reservoirs.
Regular monitoring of stratospheric NO2 started in 1980 with the
deployment of Zenith DOAS (Differential Optical Absorption Spectroscopy)
scanning spectrometers at Lauder, New Zealand, by Mckenzie and Johnston
(1982), followed by two Antarctic stations (Scott Base at 78∘ S by
Mckenzie and Johnston, 1984, and Dumont D'Urville at 66∘ S by
Pommereau and Goutail, 1988). In the following decades, instruments were
installed at remote, unpolluted sites for monitoring NO2,
O3 and other stratospheric gases suitable for ground-based
measurements.
Recently, the interest in trends of stratospheric trace gases has increased,
since global circulation models predict an acceleration of the stratospheric
residual circulation (or Brewer–Dobson circulation, BDC hereafter) of
2–3.2 %decade-1 as a consequence of the temperature changes in
the atmosphere due to human activities (Butchart, 2014). The speed of the BDC
circulation influences the concentrations of long-lived trace gases with
sources in the troposphere (e.g. CFCs, CH4, N2O), as well
as their eventual stratospheric products (Cook and Roscoe, 2009). In
particular, N2O, which is the primary source of NOx,
dissociates in the stratosphere via a reaction with excited atomic oxygen
(1D) and produces NO. NO2 is mainly formed by the
NO+O3 reaction. An accelerated BDC implies a lower
mean residence time of N2O in the stratosphere and, consequently, a
lower N2O to NO2 conversion.
On the other hand, the emissions of N2O to the atmosphere are steadily
growing, with the concentration increasing at a rate of 2.5 %decade-1
and more in recent years (IPCC, 2013). Since the N2O
lifetime in the troposphere is approximately 120 years and N2O is the
major source of NO2 in the stratosphere, a tropospheric increase will
result in an NO2 increase in the stratosphere.
These two counteracting processes could minimise or even cancel the potential
changes in the stratospheric concentration of NO2. However, the
observational evidence of a trend in the BDC speed remains inconclusive
(Butchart, 2014; Fu et al., 2015). Oberländer-Hayn et al. (2016) suggest
that, even though the stratosphere is changing substantially in response to
greenhouse gas (GHG) increases, most of the BDC trend is associated with a
lifting of the entire atmospheric circulation instead of an accelerating
flow. If this is the case, a NO2 increase should be seen in the
long term, given that all other factors remain the same.
Previous studies to establish a trend in stratospheric NO2 based on
the DOAS data from the stations with long data availability, however, did not
yield conclusive results on the global scale. A first attempt was undertaken
by Liley et al. (2000) using the longest data series available, who found an
approximately 5 % increase at Lauder (45∘ S) for the period
1981–1999. Fish et al. (2000) found no explanation related to the changes in
O3, temperature or water vapour, concluding that only changes in
the stratospheric aerosol load could explain the observations. Later,
McLinden et al. (2001) reproduced the observations of Liley et al. (2000)
using a 3-D CTM model, including halogen chemistry and assuming a negative
trend in temperature of 0.5 Kdecade-1. A few years later,
Gruzdev (2009) analysed 23 stations from the NDACC database. Trends were
found to be mostly positive in the middle and low latitudes of the Southern
Hemisphere (SH) and mostly negative in the European sector of the middle
latitudes of the Northern Hemisphere (NH). In the high and polar latitudes of
both hemispheres, the annual estimates of the trends were mostly
statistically insignificant. In Antarctica, a positive NO2 trend
was observed at 78∘ S, while at the NH high latitudes, both positive
and negative trends were observed. A detailed study of the Jungfraujoch
station (55∘ N, 3550 m a.s.l.), including DOAS plus FTIR
data and satellite composites (Hendrick et al., 2012), found negative trends
ranging from -2.4 to -4.3 %, depending on the instrument and
selected period. In summary, until now, the global NO2 trend over
the past few decades has not been accurately established. One important
difficulty in comparing the trends at different latitudes is that the
stations do not cover the same periods. In addition, the trends can be the
result of multiple factors that do not behave linearly.
The purpose of this work is to contribute to the knowledge of the long-term
NO2 evolution by searching for significant trends in the
stratospheric NO2 using the INTA NO2 DOAS records in
combination with a multiple regression model. Such models have been
extensively used in recent years to infer trends in long-term atmospheric
time series of ground-based instruments; in particular, they have been used
to study the evolution of stratospheric ozone (Bodeker et al., 1998;
Wohltmann et al., 2007; Fioletov, 2008; Mäder et al., 2010),
NO2 (Van der A et al., 2006, 2008; Gruzdev, 2009; Hendrick et al.,
2012) and other species (i.e. Remsberg, 2015).
The paper is organised as follows. In Sect. 2, the instruments and data are
described. In Sect. 3, the regression model and explanatory variables are
presented. Section 4 addresses the results and is composed of three
subsections. In Sect. 4.1, the general results are shown. Section 4.2
addresses a more detailed analysis of the subtropical case and, in Sect. 4.3,
the interhemispheric asymmetry is discussed. Finally, the conclusions are
shown in Sect. 5.
Instrumentation, stations, techniques and database
In the year 1993, INTA installed a scanning spectrometer at the subtropical
high-mountain observatory of Izaña (28∘ N, 16∘ W,
2370 m a.s.l.) for long-term measurements of stratospheric
NO2. In 1994, two identical instruments were deployed in the
Subantarctic (Ushuaia station; 55∘ S, 68∘ W) and the
Antarctic Peninsula (Marambio station; 64∘ S, 56∘ W). Then,
in 1995, a fourth instrument was installed in Antarctica at a higher latitude
(Belgrano station; 78∘ S, 35∘ W). The three instruments
cover the latitudinal belt from the outside to the inside of the Antarctic
vortex. Since installation, all instruments have been operated without
interruption.
Belgrano (in continental Antarctica) is usually closest to the core of the
polar vortex and hence is representative of the in-vortex air. Marambio
(Antarctic Peninsula) is frequently located at the edge of the vortex region
and alternates between measuring the vortex and the mid-latitude air masses.
Ushuaia is, essentially, a mid-latitude station and only occasionally is
reached by in-vortex air.
The instrumentation consists of spectrometers covering the visible range, and
the retrieval is based on the DOAS technique. At all four stations, identical
scanning spectrometers (EVA) were initially installed. In Izaña, a second
spectrometer (RASAS), based on a diode array detector, covering a wider
range, was added in 1999. Then, in 2010, the PDA was replaced by a CCD
(RASAS-II) including MAXDOAS capability. All instruments were developed at
the INTA laboratories. Data merging was carefully carried out after a period
of overlap to ensure the smooth transition between the instruments. The
transition between EVA and RASAS has been previously reported (Gil et al.,
2008). The transition between RASAS and RASAS-II required a RASAS correction
due to a degradation of the detector response since mid-2006, at a rate of
4.33 %year-1. The details of the RASAS-II instrument have been
previously published in Puentedura et al. (2012) and Robles-Gonzalez et
al. (2016). The spectrometers were installed in the top terrace of the
Izaña Atmospheric Observatoy, run by the CIAI (Centro de
Investigación Atmosférica de Izaña), belonging to the Agencia
Estatal de Meteorología (AEMET, Spain), on the slopes of the Teide
volcano, Tenerife, Canary Islands. The Izaña Atmospheric Observatory is a
high mountain station, part of the Global Atmospheric Watch (GAW) programme
and managed by the CIAI.
The EVA instrument is a scanning spectrometer for twilight measurements
between 88 and 92∘ solar zenith angles (SZAs) in the
430–450 nm spectral range, with a spectral resolution of
1 nm. These instruments are located outdoors in thermostatic
housings. The three Southern Hemisphere spectrometers were compared to each
other after 1 year of measurements with the help of a NO2 cell
containing a known amount of gas. The discrepancies among them were found to
be below 4 % (Yela et al., 2005). Twilight (AM – ante meridiem – and
PM – post meridiem) vertical column
densities were derived from all available measurements between 89 and
91∘ SZA, typically 5–6 data per twilight. New MAXDOAS instruments
have been installed at the three stations, in 2011 at Belgrano, in 2015 at
Marambio and in 2016 at Ushuaia. Both DOAS and MAXDOAS instruments
simultaneously measure for data series homogenisation.
The NO2 column retrieval is based on the standard DOAS spectral
analysis (Platt and Stutz, 2008), performed using software developed at INTA.
The DOAS settings for the NO2 column retrieval follow the NDACC
UV/Vis Working Group recommendations (Van Roozendael and Hendrick, 2012)
whenever possible. Absorption cross sections of O3, NO2,
H2O and O4 have also been included in the analysis. A
Raman scattering cross section was generated by the Win-DOAS package (Fayt
and Van Roozendael, 2001), calculated from Raman theory. Finally, the inverse
of the reference spectrum was included as a pseudo cross section to account
for stray light inside the spectrograph and the residual dark current of the
detector. The air mass factor (AMF) used for the conversion of the
NO2 slant columns to vertical columns is the NDACC NO2
standard AMF, available on the NDACC UV-Vis web page
(http://ndacc-uvvis-wg.aeronomie.be/) and based on the Lambert et
al. (1999) and (2000) climatology of the NO2 profiles. This
climatology consists of a Fourier harmonic decomposition of the UARS HALOE
v19 and SPOT-4 POAM-III v2 NO2 profile data. The cross sections and
other parameters used in the analysis are shown in Table 1.
DOAS retrieval settings.
Izaña: EVA/RASAS/RASAS-IIEVA: Ushuaia/Marambio/BelgranoFitting intervalEVA: 430–450 nm430–450 nmRASAS-I: 450–533 nmRASAS-II: 430–520 nmNO2 cross sectionVandaele et al. (1998), 220 KVandaele et al. (1998), 220 KO3 cross sectionBogumil et al. (2001), 223 KBogumil et al. (2001), 223 KH2O cross sectionHitran (Rothman et al., 2009)Hitran (Rothman et al., 2008)O4 cross sectionEVA/RASAS. Greenblatt (1990), room temp.Greenblatt (1990), room temp.RASAS-II. Hermans et al. (1999), room temp.Ring effectChance and Spurr (1997)Chance and Spurr (1997)Orthogonalisation polynomialEVA: 2nd degree2nd degreeRASAS/RASAS-II: 3rd degreeOffset correctionInverse of the referenceInverse of the referenceAMF calculationNDACC NO2 AMF LUTsNDACC NO2 AMF LUTsDetermination of the residual amountModified Langley plotModified Langley plotin the reference spectrum(Vaughan et al., 1997)(Vaughan et al., 1997)SZA range for the twilight averagingEVA: 88–91∘ SZA88–91∘ SZAof the vertical columnsRASAS/RASASII: 89–91∘ SZA(Approx. six measurements)(Approx. six measurements)
The estimated overall errors in the individual measurements are, on average,
approximately 12 % (1–2 % fit analysis; <2%
cross sections; 2 % reference spectrum; 2–3 % AMF;
2 % stratospheric temperature). For details, see Gil et al. (2008).
Fit analysis, cross sections and reference spectra do not affect the trends.
Potential long-term changes in the stratospheric temperature could have a
minor effect on the effective cross sections, but no statistically
significant changes in the stratospheric temperature have been observed
during the data period. Only differences in the NO2 profiles with
respect to the AMF climatology used could have an effect of a few tenths of a
percent.
The spectrometers are NDACC-qualified instruments (more information is
available at http://www.ndsc.ncep.noaa.gov/data/) and have been
successfully intercompared for NO2 in ad hoc international exercises
(Roscoe et al., 1999, 2010; Vandaele et al., 2005).
The DOAS technique at the zenith during twilight is slightly sensitive to
clouds. Gaps in the data are mostly due to instrumental malfunctions. For
monthly mean data, the rates of failures are 3.45, 0.40 and 0.79 %
of the total dataset for Izaña, Ushuaia and Marambio, respectively.
Multiple regression and proxies
In the present work, a multiple linear regression model of the following
form was used:
Yt=a+∑k=12β2k-1sin2πtk12+β2kcos2πtk12+∑j=5m+4βjXjt+ε(t),
where
Y(t) is the NO2 column at time t,
a is a constant,
Xj is each of the explanatory functions,
βj is the coefficient of the corresponding explanatory
function Xj,
t is the time since the start of the measurements, in months,
ε is noise as a function of time t, and
m is the number of explanatory functions.
The bracketed terms represent the harmonic functions accounting for the
annual and semi-annual waves, whereas the βX terms include all
explanatory variables. In the first run, seven proxies that could potentially
affect the NO2 distribution are used (the trend, stratospheric
aerosols, solar cycle, Quasi-Biennial Oscillation – QBO, stratospheric
temperature, stratospheric circulation, and El Niño–Southern Oscillation
– ENSO). Following Mäder et al. (2007), an iterative process was used to
exclude proxies with confidence intervals below 90 %. For details,
see Mäder et al. (2007) and Knibbe et al. (2014). In this way, the degrees of
freedom of the regression increase.
Autocorrelation affects the linear trend calculation by increasing the
uncertainty. If autocorrelation of the data noise is not included, the
standard deviation (SD) of the trend estimate will substantially
underestimate the actual uncertainty. According to Weatherhead et al. (1998),
the standard deviation of the trend per year to be used to describe the
precision of the trend estimate can be approximated quite accurately by
σβ≈σNn3/21+ϕ1-ϕ,
where σN is the standard deviation of the residuals (differences
between the NO2 data series and the modelled one), n is the length
of the data series in years and n is the autocorrelation in the residual
for time lag 1, defined as Corr (Nt, Nt-1). This formula has
previously been used for trends in NO2 (Van der A et al., 2006,
2008; Hendrick et al., 2012).
Stratospheric aerosols. Stratospheric aerosols (SA) affect the
amount of available NOx. An increase in the aerosol loading due to
volcanic eruptions reduces the NOx/NOy partitioning through the
heterogeneous hydrolysis of N2O5 on the aerosol surfaces (Fahey
et al., 1993). The eruption of Mount Pinatubo in 1991 produced a reduction in
the total NO2 column of 19–34 %, depending on the
station, in the following years (Gruzdev, 2014). Therefore, SA have been
included as a proxy in the analysis. The dataset is the monthly aerosol
optical depth (AOD) for the NH at 0.55 µm, compiled at NASA's
Goddard Institute for Space Studies (GISS). The dataset ending in
September 2012 has been extended in time to April 2014 by the optical
spectrograph and infrared imaging system (OSIRIS) data and to the end of 2014
by the white light optical particle counter (WOPC) data (Kremser et al.,
2016). For the analysed period, SA proxy accounts for the short-range
contribution of the volcanic aerosols to the NO2 column which
would, otherwise, affect the long-term trend.
Solar cycle. The solar radiation flux at 10.7 cm
(2800 MHz) is an excellent indicator of solar activity. Unlike many
solar indices, it can be easily and reliably measured
on a day-to-day basis from the Earth's surface in all types of weather. The
11-year solar cycle proxy is included to account for potential chemical
changes due to variations in UV radiation. The data used
are from the Penticton Radio Observatory in British Columbia.
QBO. The Quasi-Biennial Oscillation (QBO) affects the circulation in
the lower stratosphere and hence the species distribution (Gray and
Russell III, 1999). Recently, QBO signatures in the variability of the middle
to upper stratosphere NO2 have been detected (Liu et al., 2011). We
employ a commonly used monthly index, built using the mean of the zonal winds
averaged from three equatorial stations (Canton islands, Singapore and
Gan/Maldives) compiled by the Berlin Free University for 50 and
10 hPa.
ENSO. The El Niño–Southern Oscillation (ENSO) has been found to
influence the distribution of minor species in the tropical lower
stratosphere up to 27 km (Randel et al., 2009). We have included as a
proxy/predictor the multivariate ENSO Index (MEI) based on the six main
observed variables over the tropical Pacific: the sea-level pressure (P),
zonal (U) and meridional (V) components of the surface wind, sea surface
temperature (S), surface air temperature (A), and total cloudiness
fraction of the sky (C). Negative values of the MEI represent the cold ENSO
phase (La Niña), while positive MEI values represent the warm ENSO phase
(El Niño). For details, see Wolter and Timlim (2011). A time lag of 1 to
6 months has been tested since the middle stratosphere may take time to
respond to tropical sea surface temperature anomalies (Sioris et al., 2014).
Stratospheric temperature. There is a growing consensus that the
global temperature in the stratosphere shows a negative trend (Schwarzkopf
and Ramaswamy, 2008); however, its magnitude is still
under debate (Seidel et al., 2016). A recent study by Randel et al. (2016)
based on satellite data shows a cooling trend of -0.1 to
-0.2 Kdecade-1 in the lower stratosphere and up to -0.5 to
0.6 Kdecade-1 in the middle stratosphere for the period
1979–2015, but most of this trend is due to the large decrease prior to
1995. After that date, no significant trend is observed (Seidel et al.,
2016).
Temperature variations in the stratosphere modify the NO2 concentration
by changing the reaction rates. To account for the potential trends related
to this effect, the stratospheric temperatures over the stations extracted
from the Interim European Centre for Medium-Range Weather Forecasts (ECMWF)
Re-Analysis (ERA-Interim) (Dee et al., 2011) have been used as a proxy.
Temperature data are extracted from the 0.25∘× 0.25∘
grid at levels corresponding to the height of the NO2 maximum at each
station, as obtained by the NO2 harmonic climatology based on HALOE
v19 and POAM-II data (Lambert et al., 1999), that is, 10 hPa for Izaña,
20 hPa for Ushuaia and 30 hPa for Marambio and Belgrano. We used the monthly
mean of the average of the 00:00 and 12:00 UT values as a proxy.
Stratospheric circulation. Changes in
BDC induce changes in the concentration of stratospheric species with
tropospheric origins. Recent climate studies have found that BDC will
intensify with the increase in greenhouse gases (Butchart, 2014). With a
faster meridional circulation, N2O of tropospheric origin has a
shorter lifetime in the stratosphere, and, as a consequence, less
N2O would be oxidised to NO (Cook and Roscoe, 2009). To
identify potential changes in the long-term NO2 evolution
associated with this effect, the eddy heat flux (EHF) (v′T′) at
100 hPa, averaged over 45–75∘ S of the hemisphere, where
the observational stations are located, has been included as a proxy in the
multiple regression analysis. EHF is proportional to the vertical component
of the Eliassen–Palm (EP) flux (Fusco and Salby, 1999; Salby and Callaghan, 2004),
which has been found to have good correlation/anticorrelation with the
extratropical/tropical O3 (Randel et al., 2002; Weber et al.,
2011). Data for the calculation of EHF were obtained from the ERA-Interim
data, averaged for every month of the time series. The cumulative effects of
the total NOx/NO2 concentration were approximated by
considering a delayed response in the EHF time series as follows (Brunner et
al., 2006):
EHFt=EHFt-1eΔtτ+EHFt,
where EHF(t) is the data obtained from the ERA-Interim winds and
temperatures and Δt is the data time unit. For the
Antarctic/Subantarctic stations τ was set to 12 for the months from
April to September and was set to 3 for the rest of the year. For Izaña
subtropical station it is set to 3 for the whole year.
In Table 2, the source of each proxy is summarised.
Sources of the explanatory variables of the multiple regression.
DescriptionSourceStratospheric aerosolshttp://data.giss.nasa.gov/modelforce/strataer/Solar cyclehttp://umbra.nascom.nasa.gov/sdb/ydb/indices_flux_raw/Penticton_Observed/monthly/MONTHPLT.OBS.Quasi-Biennial Oscillation (50 and 10 hPa)http://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat.El Niño–Southern Oscillationhttp://www.esrl.noaa.gov/psd/enso/mei/table.html.Strat. temperature and wind for the eddy heat flux calculationhttp://apps.ecmwf.int/datasets/data/interim-full-daily
NO2 vertical column density time series (blue lines) and
fit obtained using the multiple linear regression model (red lines).
Fit residuals: observations minus models without trends. Red
lines: AM data. Blue lines: PM data.
Example of the contributions of the proxies other than the
seasonality and trends to the modelled NO2 time series for
Izaña.
Impact of the computed trends per month depending on the selected
proxies. Case “All” means that all proxies are included. Labels indicate
the proxy/ies excluded from the analysis. The trend obtained in the final
analysis is plotted as a reference (grey line). See the text for details.
Dependency of the Izaña PM trend on the selected period. In the
central point, the complete 1993–2014 time series is used. Each point to the
left (orange circles) is the trend after reducing the time series by 1 year
before 2014. Each point to the right (yellow circles) is the trend after
reducing the time series by 1 year after 1993.
ResultsGeneral
The multiple regression fit is shown in Fig. 1 for Izaña, Ushuaia and
Marambio. The model explains between 86 and 96 % of the observed
variance in all cases. The dominant pattern is, as expected, the seasonal
wave, explaining between 57 and 85 % of the variance, followed by
the linear trend, explaining from 6 to 27 % of the variance. The
rest of the explanatory variables have modest contributions. The residuals
and the trend were plotted together to search for common structures that
could represent anomalies not captured by the model (Fig. 2). The morning and
evening residuals look very similar, showing the non-random character of the
departures of the model. Cross-correlation between the stations shows
independent residuals (correlation coefficients: IZO/USH =+0.01,
IZO/MAR =-0.08, and USH/MAR =+0.13). The trend is significant
at 99 %.
The results of the analysis show that the seasonal waves and trend are the
major contributors to the variance, explaining 84.3/83.4, 91.0/92.2 and
89.2/94.8 % of the variance for AM/PM at Izaña, Ushuaia and
Marambio, respectively. As an example, Fig. 3 shows the individual
contributions of the less relevant proxy terms (other than the seasonal and
trend terms) for Izaña. They all maintain standard deviations below
5×1013moleccm-2, representing less than 2 %
of the NO2 data series mean value (2.45×1015moleccm-2). The details of the trend analysis are
summarised in Table 3.
To further explore the contributions of each proxy to the retrieved trend, a
sensitivity test has been performed for data of the three stations. The
results are shown in Fig. 4. Each data point represents the trend obtained
when different proxies are excluded from the analysis. The first data point
is the trend inferred considering all proxies from the monthly data series.
The next six data points represent the trends when single proxies are
excluded from the analysis, followed by the results obtained when excluding
successively more proxies in order of their significance. Finally, the last
data point represents the result of a simple linear regression. The results
show that only stratospheric aerosols have a large impact at all three
stations. Although the influence was restricted to the first years of the
data (1993–1995), before the decay of the large sulfate
aerosols load injected in the stratosphere following Pinatubo's eruption in
1991, this influence does affect the trends of the complete series by 14 to
22 %. Since the stratospheric aerosols reduce NO2, when
removing the aerosol proxy, the trend increases with a positive trend (NH)
and decreases when the trend is negative (SH). The EHF has no impact or
significance and was removed from the final analysis. The impact of the
stratospheric temperature is also very small, probably because the impact is
accounted for by the seasonal waves. The rest have minor impacts (<5%).
Next, we explored the trend sensitivity to the data series length by reducing
the period at both the starting and ending months. For this exercise, we
chose the Izaña PM series. The results (Fig. 5) show that the trend
remains essentially unchanged if the data series is shortened by up to
5 years at the start and up to 4 at the end, providing confidence in the
stability of the trend. It can also be seen that the trend is largest during
the last decade (2003–2014).
The decadal trends obtained from the mean AM and PM values for Izaña,
Ushuaia and Marambio are +8.7 ± 1.2, -8.7 ± 1.4 and
-13.7 ± 2.2 %, respectively. Most of the observed trends
occurred in the last decade (after 2003–2006), as revealed by Fig. 2. At
Izaña, the AM decadal trend is larger than the PM trend,
+9.5 ± 1.2 % vs. +7.9 ± 1.1 %,
respectively. McLinden et al. (2001), using a chemical transport model (CTM)
to explain the NO2 trend observed at Lauder (Liley et al., 2000),
found that the larger trends derived from the AM data are caused by
decreasing O3, which, in turn, reduces the rate of
NO2+O3, allowing for more NO2 to be present the
next morning. At Izaña, the different AM and PM trends cannot be
explained by changes in O3 since a non-significant decadal trend of
+0.5 ± 1.2 % is obtained for the period of 1999–2012 by the
FTIR instrument (Vigouroux et al., 2015). However, McLinden et al. (2001)
predicted a decrease in the NO2 trend of approximately
1 %, when exclusively considering the increasing halogens in the
stratosphere between 1980 and 2000. Data from the past few decades show the
opposite trend. Equivalent effective stratospheric chlorine (EESC) gas
concentrations at the mid-latitudes have declined by 15 % from their
peak values in the year 2000. In particular, HCl, the main reservoir of
inorganic chlorine, displays a negative decadal trend of
-5.9 ± 1.5 % between 1997 and 2012. The same declining rate
is observed for bromine between 2000 and 2012 (WMO, 2014). The observed
changes in both halogens should result in an increase in the NO2
trend. Moreover, the halogen decline would modify the daytime NOx
partition, since NO2 would increase during the illuminated hours
and decrease in darkness (McLinden et al., 2001), creating a larger trend at
dawn than at dusk, as is observed. In the SH, the AM and PM decadal trends
are closer, -8.7 + 1.4 and -8.7 ± 1.3 % for Ushuaia,
and -13.8 ± 2.1 and -13.6 ± 1.9 % for Marambio, for
AM and PM, respectively. At these stations, the decrease in halogens is
probably compensated for by an increase in O3. Details of the trend
analysis are summarised in Table 3.
Summary of the trend analysis for the DOAS stations. In the lower
part, the contribution of each explanatory variable to the total variance is
given in %. The colours denote the significance (bold 99 %,
italic 95 % and bold italic 90 %). “Not” means it has
been excluded from the definite analysis.
Izaña (28∘ N) Ushuaia (55∘ S) Marambio (64∘ S) AMPMAMPMAMPMTrend (in %)+9.51+7.89-8.72-8.71-13.94-13.66Uncertainty (in %)1.191.111.421.332.091.95Uncertainty (in molcm-2decade-1)2.61×10133.79×10133.73×10135.02×10135.95×10136.75×1013Autocorrelation coeff.0.4780.5250.3710.5260.4570.555Residual SD1.53×10142.08×10142.42×10142.67×10143.50×10143.43×1014Contribution to the variance and confidence level (in %) Trend27.8226.966.8913.338.5010.47Annual wave56.6155.5985.0776.4880.7484.37Semiannual waveNotNot0.620.556.091.08Strat. aerosols0.290.750.120.170.340.24Solar cycle0.240.010.320.030.180.20QBO0.510.950.290.30NotNotStrat. temperature0.951.190.834.97NotNotEHF0.001.28NotNotNotNotENSO0.410.080.000.06NotNotTotal variance explained (%)86.8386.8194.1495.8995.8496.37Contributions other than those of season and trend (%)2.404.262.552.880.520.44
99 %;
95 %;
90 %
At Belgrano, the trend has been obtained by averaging the decadal trends for
the individual months, when data are available. Since DOAS requires SZAs
close to 90∘, only the February–April and August–November periods
are suitable for measurements. For the sake of simplicity, we will refer to
these periods as the summer and winter seasons, respectively. During the
winter, the NO2 column reaches values close to zero, and its trends
are non-significant. The mean summer decadal trend is
-11.3 ± 4.0 % (-10.9 ± 4.1 and
-11.7 ± 4.0 % for AM and PM, respectively). In Fig. 6, these
results are summarised.
An alternative way of analysing the trends of the twilight data is to
consider the evolution of the NO2 diurnal build-up per hour (DBU).
The DBU is essentially due to the N2O5 photodissociation, which
is dependent on temperature, O3, and hours of the night, and can be
expressed as
DBU=NO2PM-NO2AM24-Δt=NO2PM24-Δt1-∑basetoaWzRz∑basetoaWz,
where NO2AM and NO2PM are the
columns at sunrise and sunset, respectively, and Δt is the monthly
mean of the diurnal night hours. R is the AM-to-PM ratio at a given height,
and W(z) is a NO2 climatological profile acting as a weighting
factor. R can be computed (Senne et al., 1996) from
Rz=NO2AMzNO2PMz=e-2K(z)O3(z)Δt,
where K is the NO2+O3 reaction rate constant and
O3(z)
is the ozone concentration at height z.
AM (red) and PM (blue) decadal trends obtained for the DOAS
stations. The trend at Belgrano was obtained for the February–March–April period.
See the text for details.
Diurnal NO2 build-up in moleccm-2h-1 for
the four DOAS stations. (a)–(c): Observed DBU (black
lines). Annual means (red lines). Linear trends (dotted lines). The computed
DBU is plotted in (a) (blue line). (d) The Belgrano DBU for
all available months (black stars). The months of March and April are
highlighted with red circles and blue squares, respectively.
NO2 decadal trends for the individual months at Izaña,
Ushuaia, Marambio and Belgrano. Open circles and open squares represent the
AM and PM data, respectively. Solid squares are the monthly means. The
Belgrano trends are from the diurnal means. The shadowed area is the mean
size of the area of the potential NAT (nitric acid trihydrated) PSCs (right scale).
At Izaña, the DBU obtained from Eq. (4) using the O3 and
temperature profiles from the local ozone sounding agree well with the
observations (Fig. 7). The resulting decadal trend for the period is
+5.5 ± 1.6 %, which provides a hint at the trends in the
reservoirs. In the Subantarctic/Antarctic stations, the DBU displays negative
decadal trends of -8.4 ± 1.8 and -13.8 ± 2.4 % for
Ushuaia and Marambio, respectively. In Belgrano, the values are too low
(winter) or the AM and PM data are too close (February) to obtain the DBU,
except in March and April. The BDU decadal trends for these months are
-13.0 ± 7.3 and -10.6 ± 2.9 %, which is probably not
representative of the mean annual trend. In summary, all SH stations exhibit
a negative decadal trend in their DBUs, ranging from -8 to
-14 %, revealing either a reduction of N2O5 in the
past few years at the middle and high latitudes of the Southern Hemisphere or
dynamically induced changes in the NO2 vertical distribution.
To investigate the variability of the NO2 trend with the season,
the calculations were performed on a month-by-month basis. Even though the
contributions of proxies other than seasonal waves are small (<1% in most cases), the same proxies and lags were applied in the
multiple regression as in the previous cases. The results are shown in
Fig. 8. The AM and PM trends were plotted separately for each station, along
with their mean value. At Izaña, during the winter and spring, both
trends agree well. From July to the end of the year, as the trend increases,
there is also a progressive increase in the AM–PM difference that clearly
exceeds the error bars. O3 trends in individual months (not shown)
are too small and do not change much throughout the year. A seasonality in
the decreasing halogen trend could be a possible explanation, but, for the
moment, we have no satisfactory explanation for this behaviour. At Izaña,
the monthly mean trends are largest in the autumn and beginning of the winter
and are mainly forced by a faster increase in the AM trends. The annual
excursion ranges from 4.4 % at its minimum in February to
13.1 % in December. The downward trends in the SH become more
negative further south. At Ushuaia, the trend is less negative to values
close to the annual minimum (-7 %), whereas in Marambio, the trend
increases with respect to the previous months (-21 %). The time
when the trends diverge coincides with the formation of the Antarctic Polar
Vortex (APV), and this divergence extends for a period coincident with the
coldest temperatures and polar stratospheric cloud (PSC) formation. In
Fig. 8, the 2006–2016 mean area covered by temperatures low enough for PSC
formation, as obtained by the Climate Prediction Center (CPC)
(http://www.cpc.ncep.noaa.gov/products/stratosphere/polar/polar.shtml),
is shown in grey. In September, the absolute maximum of the negative trends
is reached at both stations as a result of an increase in the number of days
that the stations were inside the APV. We will come back to this point later.
Annual evolution of the number of days that the stations spent
inside the polar vortex at 530 K according to the Nash et al. (1996)
criteria.
During the autumn and winter, there is meridional transport towards the pole.
Once the Antarctic vortex is established, O3 and NO2
transported from lower latitudes accumulate outside the vortex, since the
vortex edge acts as a barrier for mass exchange. Marambio is generally at the
edge of the vortex, whereas Ushuaia remains outside, with very few
exceptions. The observed behaviour could be explained by a positive trend in
the size of the vortex area, such that Marambio remains inside the vortex
longer, thus reducing its NO2 column year by year. Conversely,
larger NO2 masses transported from lower latitudes would accumulate
above Ushuaia, somehow compensating for the negative trend. Alternatively,
the same effect could be caused by a drift of the average vortex position
towards the Atlantic/Argentinean sector. The drifting of the APV during the
spring has been studied over the past decade (Hassler et al., 2011; Grytsai
et al., 2017) using both observations and models. The results show
longitudinal displacements of the APV across the years. In particular, the
minimum of the planetary wavenumber 1, centred at latitudes of approximately
65∘ S, has shifted westward since 2003, thus increasing the time of
the stations in the Argentinean sector under the influence of the PVA. To
confirm this effect over Ushuaia and Marambio, the NO2
representative potential temperature level of 530 K between days 150
and 330 (May through to November) has been used to calculate the position of
the station with respect to the vortex according to the widely accepted Nash
et al. criteria (Nash et al., 1996), based essentially on finding the
maximum PV gradient at equivalent latitudes to define the vortex edge.
ERA-Interim data were used for this purpose. The results show an increasing
number of days inside the vortex in both Ushuaia and Marambio in previous
years (Fig. 9). To test whether the impact of the vortex position drifting is
the main cause of the wavy structure in the trend observed in the Ushuaia
winter (small trends in July–August, large ones in September–October; see
Fig. 8), we have repeated the monthly trend analysis, excluding the days when
the station was inside the vortex. Results show that while, as expected,
negative trends are reduced, the magnitude is not enough to justify the
observed trends. In fact, the changes are only a few tenths of a percent,
probably due to the fact that the days inside the vortex at Ushuaia, while
increasing along time, are still very small compared with the total number of
days. In summary, when only extra-vortex data are used, the winter wavy
structure remains, providing evidence that the evolution of the seasonal
NO2 trend is dominated by the mid-latitude dynamics along the year.
The trends at Belgrano in the summer are lower than those at Marambio, with
values close to those at Ushuaia. The winter data are not significant. The
NO2 concentration in the stratosphere during these months is very
low and very dependent on the time of the last warming, which is highly
variable from year to year.
Instrument information.
InstrumentPlatformTechniqueDataPeriodData availabilityFTIRGround-basedFTIR1/2000–12/2013IMK/ASF NDACC-Izaña FTIR team https://www.imk-asf.kit.edu/english/201.phpMIPASENVISATFTIR7/2002–4/2012IMK/IAA MIPAS team http://www.imk-asf.kit.edu/english/308.phpSCIAMACHYENVISATDOAS8/2002–3/2011http://www.iup.uni-bremen.de/doas/scia_no2_data_acve.htmOMIAURADOASV3.01/2005–12/2014http://avdc.gsfc.nasa.gov/pub/most popular/overpass/OMI/OMNO2/
NO2 VCD annual means at Izaña from the available
datasets (a) obtained as monthly averages (b).
The subtropical case: comparisons with other observations
DOAS trends at Izaña have been compared with the ground-based NDACC FTIR
data located in the same station and with three satellite instruments: nadir
SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric
CHartographY) and OMI (Ozone Monitoring Instrument), both DOAS
spectrometers, and the limb sounding FTIR MIPAS (Michelson Interferometer for
Passive Atmospheric Sounding). The SCIAMACHY stratospheric data are produced
by the IUP-Bremen. The data used here are the improved version V3.0 released
in 2016. The MIPAS NO2 profiles are from IMK/IAA data
version v5h NO2 20/v5r NO2 220 at the station's location converted to VCD.
Only the daytime data (10 AM overpasses) were considered. The MIPAS data have
been combined with the DOAS averaging kernels to make them comparable. The
basic instruments are shown in Table 4. Further details can be found on the
corresponding web pages. Figure 10 shows the annual mean values (upper panel)
obtained from the monthly means (lower panel) to avoid potential biases due
to a non-uniform distribution of data available across the year. This is
particularly important for the ground-based FTIR data, since there are many
more daily measurements during the summer months, when NO2 is at
maximum, than in other months. Only in the few cases when the monthly data
were not available were the corresponding climatological values used. No
photochemical correction has been applied to the satellite data to refer them
to the same SZA because the purpose is to compare the NO2 column
trends. The differences between the instruments for the monthly means are
within 5 % and exhibit similar seasonalities (Fig. 10, lower panel).
There are multiple causes affecting the magnitude of the measured column,
namely, the hour of the measurements, field of view, and vertical sensitivity
(Piters et al., 2011; Robles-Gonzalez et al., 2016); however, these factors
should not affect the observed trends. Table 5 displays the results of the
trends from the regression model using the monthly data. There is a large
scatter in the trends. The MIPAS decadal trend is
+5.0 ± 3.5 %, close to that of FTIR
(+3.6 ± 2.7 %). SCIAMACHY provides a low trend but is
statistically non-significant. OMI shows a decadal trend of
+7.5 ± 2.2 %, not far from that obtained from DOAS but for
the 2002–2014 period. MIPAS and OMI are significant at the 90 %
confidence level. In summary, at present, there is no agreement between the
instruments and the actual trend over Izaña. To further complicate the
interpretation, McLinden et al. (2001), using a 3-D CMT model, found that the
trend can be highly dependent on the time of day of the measurement. For the
case shown in their paper, i.e. with decreasing O3 and increasing
halogens, there is almost a factor of 2 between the maximum trend at sunrise
and the trend minimum at sunset. In the late morning and around noon, when
the satellites and FTIR measurements are taken, the trend is much lower than
that at SZA = 90∘ AM, when the DOAS takes its measurement.
Therefore, unless careful analyses of the CTM models are carried out for the
exact periods of the measurements, the trend intercomparisons of a set of
measurements obtained at different times of day has been found to be of
little use for the confirmation of the DOAS trend. There is little doubt,
however, of the sign of the trends since all instruments show a positive
trend.
Discussion of the hemispheric asymmetry
The opposite sign in the NO2 trends observed at the NH and SH
stations shows that the NOx distribution in the stratosphere does not
directly reflect the increasing N2O in the atmosphere, at least
when individual stations are analysed. Stratospheric temperatures from the
ECMWF ERA-Interim data above the NH and SH stations show no statistically
significant trends during the period of observation. The EHF has been used as
a proxy for the meridional transport as a possible explanatory variable for
the trends observed in the analysis, but no significant correlation has been
obtained. Eckert et al. (2014) found a similar hemispherically asymmetric
pattern when analysing the global ozone trends from MIPAS. Negative trends
were found in the northern lower stratosphere, whereas positive values were
observed in the Southern Hemisphere. These authors suggested a meridional
displacement of the subtropical barriers as the cause of the hemispheric
asymmetry, since they could mimic the observed trends by shifting the
subtropical mixing barriers to the south by 5∘ at altitudes below
30 km, although no explanation was provided for the displacement
beyond what is possible due to low-frequency natural variations. Previously,
Stiller et al. (2012) computed the age of stratospheric air (AoA) in the
stratosphere using MIPAS sulfur hexafluoride (SF6) measurements, finding
a positive trend in the Northern Hemisphere, centred at 20 km from 20
to 60∘ N, and a negative trend in the tropics and the Southern
Hemisphere. In particular, the increasing AoA was observed at heights from
the tropopause to the upper limit of MIPAS (38 km). The MIPAS AoA has
been reassessed (Haenel et al., 2015), but the hemispheric asymmetry remains.
Total positive reactive NOy trends in the Northern Hemisphere and the
negative trends in the Southern Hemisphere in the lower stratosphere (∼25km) have been reported by Funke et al. (2015), using the MIPAS
data from the period 2002–2012. A more detailed meridional structure of the
NO2 trends was obtained by Burrows et al. (2016) by analysing the
SCIAMACHY NO2 limb measurements in 2003–2011. They find a
well-defined region with positive/negative trends extending from the Equator
to the high latitudes in the Northern Hemisphere/Southern Hemisphere, with a
maximum at 26–27 km in the subtropics.
Since the global NOy remained almost constant during this period, the
observed behaviour should be the result of a meridional redistribution.
Recently, Stiller et al. (2017) found the same meridional pattern in
N2O from the CLaMS CTM model, driven by the ERA-Interim reanalysis,
and explored the hemispheric symmetry in the AoA, confirming a southward
displacement of the stratospheric circulation pattern between the potential
temperature levels of 500 and 800 K. Additionally, Garfinkel et
al. (2017), using the GEOSCCM model, also found an increase in the Northern
Hemisphere AoA for the period after 1992 when including the stratospheric
aerosols and ODS evolution in the simulation. The positive trend peaks
between 20 and 30∘ N and extends from 20 to 40 km.
In summary, previous works exhibit discrepancies in the heights where the
stratospheric trends peak (AoA, ozone and NO2) but agree on the
existence of a change in sign between hemispheres in the stratospheric trend.
Although the search for an explanation of the observed hemispheric asymmetry
patterns is outside the scope of this paper, there is growing evidence based
on observational analysis and modelling of a redistribution of tracers in the
stratosphere in the past few decades. The results presented here provide an
additional confirmation of the changes in the dynamics of the lower/middle
stratosphere based on data from an independent source.
Conclusions
Long-term datasets from the DOAS spectrometers located at remote stations
(Izaña, 28∘ N, Ushuaia, 55∘ S, Marambio,
64∘ S, and Belgrano, 78∘ S) have been used to calculate the
trends in the NO2 stratospheric column over the 1993/94–2014
period. Seasonal cycles, stratospheric aerosols, solar cycles, ENSO, QBO,
stratospheric temperature, and eddy heat flux were included as explanatory
variables. The results show a positive mean NO2 decadal trend of
8.7 ± 1.2 % over the past 23 years at the northern subtropical
station at Izaña, surpassing the expected 2.5 % increase due to
the positive N2O trend. The observed trend is larger at dawn
(9.5 ± 1.2 %) than at dusk (7.9 ± 1.1 %). The
attempt to compare other available databases does not provide confirmation of
the observed DOAS trend, due to either the shorter time series, larger
uncertainties, or different diurnal sampling. The same analysis applied to
the Antarctic/Subantarctic stations displays the opposite trends. The
NO2 stratospheric column has been found to decrease at a mean rate
of -8.7 ± 1.4 and -13.8 ± 2.2 %decade-1 for
Ushuaia and Marambio, respectively. Most of the observed trends occurred in
the last decade (after 2003–2006). Belgrano also displays a negative trend
of -11.3 ± 4.0 % for the summer/autumn season, which is the
only period that is statistically significant. The trend in the NO2
diurnal build-up (DBU) rate is essentially due to N2O5
photolysis and is +5.5 ± 1.6 % at Izaña,
-8.4 ± 1.8 % at Ushuaia and -13.8 ± 2.4 %
at Marambio, providing a hint about the NOy trend.
The analysis of individual months shows that the trend is largely dependent
on the season. The decadal trends' annual excursion at Izaña ranges from
4.4 ± 1.8 % at its minimum in February to
13.8 ± 2.8 % in December. The Southern Hemisphere station
trends are strongly influenced by the Antarctic polar vortex. The negative
trend at Marambio increases in mid-winter (-21 %), whereas at
Ushuaia, the decadal negative trend is reduced to values close to its annual
minimum during the same time (-7 %). The largest trend is observed
in September. Analysis excluding the APV air data indicates that the shift of
the APV toward the South American sector observed in recent years has only a
minor contribution in the trend differences between stations.
The results presented here provide an additional confirmation of the changes
in the lower/middle stratosphere dynamics based on data from an independent
source and provide 30 further observational evidence of the recent findings
on the hemispheric asymmetry in stratospheric NO2 and the age of air during
the past decades.
Izaña DOAS data are available via ftp at the NDACC database ftp://ftp.cpc.ncep.noaa.gov/ndacc/station/izana.
Belgrano, Marambio and Ushuaia DOAS data are available from the corresponding authors upon
request.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Twenty-five years of
operations of the Network for the Detection of Atmospheric Composition Change
(NDACC) (AMT/ACP/ESSD inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
The authors want to acknowledge the station operations teams, who were
particularly valuable at the remote locations. The FTIR data were provided by
the Izaña-FTIR team at the Karlsruhe Institute of Technology
(Thomas Blumenstock). Long-term measurements were made possible thanks to the
funding provided by EU Framework programme NORS
(FP7/2007-2013 under grant agreement no. 284421) and Spanish R+D Plan
projects AMISOC (CGL2011-24891), AVATAR (CGL2014-55230-R), VIOLIN
(CGL2010-20353) and HELADO (CTM2013-41311-P). The trend analysis was
programmed with the GNU OCTAVE version 4.2.0 free software. Edited by: Hal Maring Reviewed by: two
anonymous referees
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