ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-4403-2018Spatio-temporal variations of nitric acid total columns from 9 years of IASI measurements – a driver studySpatio-temporal variations of HNO3 total columnsRonsmansGaétanegronsman@ulb.ac.beWespesCatherineHurtmansDanielClerbauxCathyCoheurPierre-FrançoisUniversité Libre de Bruxelles (ULB), Faculté des Sciences, Chimie Quantique et Photophysique, Brussels, BelgiumLATMOS/IPSL, UPMC Univ. Paris 06 Sorbonne Universités, UVSQ, CNRS, Paris, FranceGaétane Ronsmans (gronsman@ulb.ac.be)3April2018187440344237November201727November201727February20181March2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/4403/2018/acp-18-4403-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/4403/2018/acp-18-4403-2018.pdf
This study aims to understand the spatial and temporal variability of
HNO3 total columns in terms of explanatory variables. To achieve this,
multiple linear regressions are used to fit satellite-derived time series of
HNO3 daily averaged total columns. First, an analysis of the IASI 9-year
time series (2008–2016) is conducted based on various equivalent latitude
bands. The strong and systematic denitrification of the southern polar
stratosphere is observed very clearly. It is also possible to distinguish,
within the polar vortex, three regions which are differently affected by the
denitrification. Three exceptional denitrification episodes in 2011, 2014 and
2016 are also observed in the Northern Hemisphere, due to unusually low
arctic temperatures. The time series are then fitted by multivariate
regressions to identify what variables are responsible for HNO3
variability in global distributions and time series, and to quantify their
respective influence. Out of an ensemble of proxies (annual cycle, solar
flux, quasi-biennial oscillation, multivariate ENSO index, Arctic and
Antarctic oscillations and volume of polar stratospheric clouds), only the
those defined as significant (p value < 0.05) by a selection algorithm
are retained for each equivalent latitude band. Overall, the regression gives
a good representation of HNO3 variability, with especially good results at
high latitudes (60–80 % of the observed variability explained by the
model). The regressions show the dominance of annual variability in all
latitudinal bands, which is related to specific chemistry and dynamics
depending on the latitudes. We find that the polar stratospheric clouds
(PSCs) also have a major influence in the polar regions, and that their
inclusion in the model improves the correlation coefficients and the
residuals. However, there is still a relatively large portion of HNO3
variability that remains unexplained by the model, especially in the
intertropical regions, where factors not included in the regression model
(such as vegetation fires or lightning) may be at play.
Introduction
Nitric acid (HNO3) is known to influence ozone (O3) concentrations in
the polar regions, due to its role as a NOx
(≡ NO + NO2) reservoir and its ability to form polar
stratospheric clouds (PSCs) inside the vortex
e.g.. In the stratosphere, HNO3
forms from the reaction between OH and NO2 (produced by the reaction
N2O + O1D) and is destroyed by reaction with OH or
photodissociation,
both of these reactions being slow
during daytime and virtually non-existent at night-time
. This leads to photochemical lifetimes
between 1 and 3 months up to 30 km altitude and around 10 days at higher
altitudes , inducing similar general transport pathways for
O3 and NOy (the sum of all reactive nitrogen species – including
HNO3) . During the polar winter, with the arrival of
low temperatures, PSCs, composed of HNO3, sulphuric acid (H2SO4) and
water ice (H2O), form within the vortex
e.g.. They act as sites for heterogeneous
reactions, turning inactive forms of chlorine and bromine into active
radicals, and leading to the depletion of O3 in the polar regions
e.g.. Furthermore, the
formation of these PSCs, particularly the nitric acid trihydrates (NAT),
leads to the denitrification of the stratosphere (condensation of HNO3
followed by sedimentation towards the lower stratosphere), which prevents
ClONO2 from reforming e.g.
and further enhances the depletion of ozone.
HNO3 has been measured by a variety of instruments over the last few
decades, of which the MLS (on the UARS, then the Aura satellite) provided the
most complete data set. MLS measurements began in 1991 and allowed for the
extensive analyses of seasonal and interannual variability, as well as the
vertical distribution of HNO3,
however with a coarse horizontal resolution. Other
instruments have also measured HNO3 in the atmosphere, such as MIPAS
(ENVISAT, ), ACE-FTS (SCISAT, ) and
SMR
in the order of the instruments cited above: Microwave Limb
Sounder (MLS), Michelson Interferometer for Passive Atmospheric Sounding
(MIPAS), Atmospheric Chemistry Experiment-Fourier Transform Spectrometer
(ACE-FTS), Sub-Millimetre Radiometer (SMR)
(Odin, ),
although few of these data have been used for geophysical analyses in terms
of chemical and physical processes influencing HNO3, mostly due to the
limited horizontal sampling of these instruments.
O3 in comparison has been extensively analysed, and numerous studies
have been conducted to provide a better understanding of the factors
influencing stratospheric O3 depletion processes and to assess the
efficiency of international treaties put in place to reduce its
extent (e.g.
).
Most recent studies have used multivariate regression analyses in order to
identify and quantify the main contributors to O3 spatial and seasonal
variations. The variables included in such regression models depend on the
atmospheric layer investigated (troposphere or stratosphere) and most often
include the solar cycle, the quasi-biennial oscillation (QBO), the aerosol
loading and the equivalent effective stratospheric chlorine (EESC) (e.g.
). They also often include
climate-related proxies for specific dynamical patterns such as El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) or the
Antarctic Oscillation (AAO) . In various
multivariate regression studies, an iterative selection procedure is used to
isolate the relevant variables for the concerned species
.
Despite the fact that it is one of the main species influencing stratospheric
O3, HNO3 has been studied much less in terms of explanatory variables,
in part because of the lack of global, consistent and sustained measurements.
Identifying the factors driving its spatial and temporal variability could consequently
help to characterize its behaviour in stratospheric chemistry, and hence
its interactions with O3.
The Infrared Atmospheric Sounding Interferometer (IASI) on-board the Metop
satellites has been, and still is, providing global measurements of the
HNO3 total column, which are used here to investigate HNO3 spatial and
temporal variability. The data set used (Sect. ) consists of a
time series of HNO3 columns retrieved from IASI/Metop A measurements over
the period 2008–2016; measurements were taken twice daily and with global
coverage. This unprecedented spatial and temporal sampling of the high
latitudes allows for an in-depth monitoring of the atmospheric state, in
particular during the polar winter . We make
use of equivalent latitudes in order to isolate polar air masses with
specific polar vortex characteristics, and therefore better understand the
role of HNO3 in polar chemistry, with regard to geophysical features such
as the extent of the polar vortex and polar temperatures
(Sect. ). We next apply multivariate regressions to the
IASI-derived HNO3 time series to statistically characterize their global
distributions and seasonal variability at different latitudes for the first
time. The global coverage and the sampling of observations also allow for the
retrieval of global patterns of the main HNO3 drivers (Sect. ).
IASI HNO3 data
The HNO3 columns used here were retrieved from measurements taken by the
IASI instrument on-board the Metop A satellite. IASI measures the upwelling
infrared radiation from the Earth's surface and the atmosphere in the
645–2760 cm-1 spectral range at nadir and off-nadir along a broad
swath (2200 km). The level 1C data set used for the retrieval consists of
measurements taken twice daily (at 09:30 and 21:00, equatorial crossing
time) at a 0.5 cm-1 apodized spectral resolution and with a low
radiometric noise (0.2 K in the HNO3 atmospheric window)
. The ground field of view of the instrument
consists of four elliptical pixels (2 by 2) yielding a horizontal footprint
(single pixel) that varies from 113 km2 (12 km diameter) at nadir to
400 km2 at the end of the swath.
To retrieve HNO3 atmospheric concentrations, we use the level 1C
measurements available in near real-time at Université Libre de Bruxelles
(ULB) and retrieved by the Fast Optimal Retrievals on Layers for IASI (FORLI)
software, which uses the optimal estimation method . A
complete description of the FORLI method can be found in
and a summary of the retrieval parameters specific to HNO3 in
. The retrieval initially yields HNO3 vertical profiles
on 41 levels (from 0 to 40 km altitude) but with limited vertical
sensitivity. The characterization of the retrieved profiles conducted by
showed that the degrees of freedom for signal (DOFS)
range from 0.9 to 1.2 at all latitudes. Because of this lack of vertical
sensitivity, the HNO3 total column is the most representative quantity for
the IASI measurements and is exploited here for the investigation of HNO3
time evolution. It is important to note, however, as thoroughly discussed
in , that the information on the HNO3 profile comes
mostly from the lower stratosphere (15–20 km) and the profile is therefore
mainly indicative of stratospheric abundance. In order to compute the total column, the retrieved vertical
profiles are integrated over the whole altitudinal range. Our previous study
showed that the resulting total columns yield a mean error of 10 % and a
low bias (10.5 %) when compared to ground-based FTIR measurements
. The data set used spans from January 2008 to December
2016 with daily median HNO3 columns averaged on a 2.5∘×2.5∘ grid, for which both day and night measurements were used. Based
on cloud information from the EUMETSAT operational processing, the
cloud-contaminated scenes are filtered out, i.e. all scenes with a fractional
cloud cover higher than 25 % are not taken into account. It should be
noted that there was an abnormally small amount of IASI L2 data distributed
by EUMETSAT between 14 September and 2 December 2010 ,
and that these data have been removed from the figures and analyses in this
particular paper. For the purpose of this study the data are divided into
several time series according to equivalent latitudes
(sometimes referred to as
“eqlat”), which allow one to consider dynamically consistent regions of the
atmosphere throughout the globe and better preserve the sharp gradients
across the edge of the polar vortex. The potential vorticity data are daily
fields obtained from ECMWF ERA Interim reanalyses, taken at the potential
temperature of 530 K. Following the analysis of the potential vorticity
contours, we consider five equivalent latitude bands in each hemisphere
(30–40, 40–55, 55–65, 65–70, 70–90∘), plus the intertropical
band (30∘ N–30∘ S), with the corresponding potential
vorticity contours, in units of 10-6 K m2 kg-1 s-1,
being 2.5 (30∘), 3 (40∘), 5 (55∘), 8 (65∘)
and 10 (70∘) (Fig. ).
HNO3 time series
The HNO3 time series for the mid to high latitudes are displayed in
Fig. for the years 2008–2016. Total columns are
represented for both north (green) and south (blue curves) hemispheres, for
equivalent latitudes bands 40–55, 55–65, 65–70 and 70–90∘. Also
highlighted by shaded areas are the periods during which the northern and
southern polar temperatures, taken at 50 hPa (light green and light
blue for the 70–90 eqlat band, N
and S respectively, and purple
for the 65–70∘ S eqlat band), were equal to or below the polar
stratospheric clouds formation threshold (195 K, based on ECMWF
temperatures). It should be noted that while this temperature is a widely
accepted approximation for the formation threshold for NAT (type I), its
actual value can differ depending on the local conditions
. Also, other forms of PSCs, particularly
type II PSCs (ice clouds), form at a lower temperature of 188 K,
corresponding to the frost point of water, or 2–3 K below the frost point
(e.g. ).
Example of equivalent latitude contours for -70 (blue), -65
(light blue), -55 (red) and -40 (green) equivalent latitudes. The
background colours are HNO3 total columns (daily mean for 21 July 2011, in
molec cm-2).
As a general rule, we find larger concentrations in the Northern Hemisphere,
for the entire latitudinal range shown here (40–90 eqlat). The hemispheric
difference in HNO3 maximum concentrations can be partly attributed to the
hemispheric asymmetry of the Brewer–Dobson circulation associated with the
many topographical features in the Northern Hemisphere compared to the
Southern Hemisphere. As a result, the Northern Hemisphere has a more intense
planetary wave activity, which strengthens the deep branch of the
Brewer–Dobson circulation. This also has a direct effect on the latitudinal
mixing processes, which usually extend into the Arctic polar region, but less so
into the Antarctic due to a stronger polar vortex
.
(a–d) HNO3 total columns time series for the years
2008–2016, for equivalent latitude bands 70–90, 65–70, 55–65 and 40–55,
north (green) and south (blue). Vertical shaded areas are the periods during
which the average temperatures are below TNAT in the north (green)
and south (blue) 70–90∘ band, and in the south (purple)
65–70∘ band. Note that the large period without data in 2010 is when
there was a low amount of data distributed by EUMETSAT (see
Sect. ). (e) Daily average temperatures time series
(in K) taken at the altitude of 50 hPa for the equivalent latitude bands
70–90∘ N (green) and South (blue) and 65–70∘ S (purple).
The horizontal black line represents TNAT, i.e. the 195 K line.
Beyond hemispheric asymmetry, we also find that HNO3 columns are generally
larger at higher latitudes, with total column maxima between
3.0 × 1016 and 3.7 × 1016 molec cm-2 in
the equivalent latitudes bands 70–90, 65–70 and 55–65∘, and lower
at around 2.2 × 1016 molec cm-2 in the 40–55∘
band, especially for the Southern Hemisphere. This latitudinal gradient of
HNO3 has been previously documented (e.g.
) and can mainly be
explained by the larger amounts of NOy at high latitudes due to a larger
age of air and the NOy partitioning favouring HNO3. An interesting
feature observable in Fig. is the different behaviour of
the three highest latitude regions with regard to the polar stratospheric
cloud formation threshold in the Southern Hemisphere. The denitrification
process that occurs with the condensation and sedimentation of PSCs (see e.g.
and for further
details) is obvious in the 70–90∘ S region (blue curve in
Fig. a), with a systematic and strong decrease in HNO3
total columns (from 3.3 × 1016 to
1.5 × 1016 molec cm-2) starting within 12 to 25 days
after the stratospheric temperature reaches the threshold of 195 K (start of
the blue shaded areas). The loss of HNO3 therefore usually starts around
the beginning of June in the Antarctic and the concentrations reach their
minimum value within one month. They stay low at
1.4 × 1016 molec cm-2 until mid-November (with a slight
gradual increase to 1.7 × 1016 molec cm-2 quite often
seen during the two following months), and start to increase again during
January, i.e. between 2.5 and 3 months after the polar stratospheric
temperatures are back above the NAT formation threshold. The same pattern can
be observed in the 65–70∘ S equivalent latitude region. However, a
delay of approximately 1 month exists for the start of the steepest decrease
in HNO3 columns, which appears to be more gradual than in the
70–90∘ S regions (3 months to reach minimum values, starting in
July). The minimum and plateau column values are thus reached by the end of
September; they remain higher than at the highest latitudes, with values
staying at around 1.7 × 1016 molec cm-2. The delayed and
less severe loss of HNO3 in the 65–70∘ S band confirms that the
denitrification process spreads from the centre of the polar vortex, where
the lowest temperatures are reached first
. This spreading from the centre
also leads to slightly higher concentrations for the maxima in the
65–70∘ S eqlat band (mean of maxima of 3.26 × 1016
versus 3.11 × 1016 molec cm-2 in the 70–90∘ S
eqlat band). The delayed decrease in HNO3 in the outer parts of the vortex
(i.e. in the 65–70∘ S eqlat band) can thus be attributed to the
later appearance of PSCs in this region (see Fig. b purple
shaded areas). By the end of December, when the vortex has started breaking
down (e.g. ), the total
columns in both eqlat bands become homogenized and reach the same range of
values (1.7 × 1016 molec cm-2).
Zonally averaged daily HNO3 total columns distribution over
2008–2016, expressed in molec cm-2. The lines represent potential
vorticity contours at a potential temperature of 530 K (5 (black), 8 (cyan)
and 10 (blue) × 10-6 K m2 kg-1 s-1) which
correspond to the equivalent latitudes contours illustrated in
Fig. .
If the decrease is slower at 65–70 eqlat, this is not the case for the
recovery, with the build-up of concentrations starting roughly at the same
time as for the 70–90∘ S eqlat band, hence resulting in a shorter
period of denitrified atmosphere in the 65–70∘ S band. These
results agree well with previous studies by and
for earlier years. However, the recovery of the HNO3
total columns is very slow compared to other species, namely O3, for which
concentrations return to usual values within 2 months (i.e. in December)
after PSCs have disappeared. In fact, the HNO3 columns stay low until well
after the September equinox and are only subject to a slow increase starting
2 months later (in early December) with concentrations back to
pre-denitrification levels by May. While more persistent local temperature
minima staying below 195 K could explain part of this late recovery, we
hypothesize that it is mainly due to a combination of two factors: (1) the
significant sedimentation of PSCs towards the lower atmosphere during the
winter, such that few PSCs remain available to release HNO3 under warmer
temperatures ; and (2) the effective
photolysis of HNO3 and NO3 in spring and summer under prolonged
sunlight conditions, mainly at the highest latitudes, which respectively
increase the HNO3 sink and reduce the chemical source (because NO3
cannot react with NO2 to produce N2O5,
). The increase observed in
March, at the start of the winter, can in turn be explained by a reduction in
the number of hours of sunlight (implying less photodissociation), as well as
by diabatic descent, which brings HNO3-rich air to lower altitudes.
It is worth noting that the two regions previously mentioned (inner and outer
vortex) have been observed to behave differently; the inner vortex
(70–90∘ S) undergoes strong internal mixing whereas the outer
vortex (65–70∘ S), isolated from the vortex core, experiences
little mixing of air. This, combined with a cooling of the stratosphere, could
lead to increased PSC formation and further ozone depletion
.
For northern (a) and southern (b) 70–90
equivalent latitude bands: HNO3 total columns time series for the years
2008 to 2016 in molec cm-2. Note that the y axis limits differ
between the two plots.
Regarding the HNO3 columns in the 55–65∘ S eqlat band, which
comprises the vortex rim, or “collar” , it is evident from
Fig. c that they are not affected by denitrification,
which is in agreement with previous observations (e.g.
). In fact, we show that columns
in this particular band keep increasing when temperatures at higher latitudes
start decreasing, to reach maximum values of about
3.4 × 1016 molec cm-2 in June–July; this is due to a
change in NOy partitioning towards HNO3, which is in turn due to less
sunlight in this period compared to summer. Also inducing increased
concentrations during the winter at high latitudes is the diabatic descent
occurring inside the vortex when temperatures decrease. This downward motion
of air enriches the lower stratosphere with HNO3 coming from higher
altitude , yielding higher column values which
are, in this eqlat band, not affected by denitrification. The slow decrease
in HNO3 starting in August and leading to minimum values in January is
related to the combined effect of increased photodissociation and mixing with
the denitrified polar air masses which are no longer confined to the polar
regions. Finally, as previously mentioned, the 40–55∘ S eqlat band
records lower column values throughout the year (generally below
2 × 1016 molec cm-2) and a much less pronounced seasonal
cycle.
High latitudes in the Northern Hemisphere do not usually experience
denitrification, mostly because the temperatures, while frequently showing
local minima below 195 K, rarely reach the PSC formation threshold over
broad areas and for long time spans (see Fig. for average
temperatures, light green vertical areas). A few years stand out, however,
with exceptionally low stratospheric temperatures. This is especially the
case of the 2011 , 2016 and, to some extent, 2014 Arctic
winters. During these three winters, temperatures dropped below the 195 K
threshold over a broader area and stayed low for a longer period than usual. Lower concentrations of
HNO3 were recorded as a consequence, especially in the northernmost
equivalent latitude band (see Fig. ). The winter
2016 recorded exceptionally low temperatures
in particular, which led to large denitrification and significant ozone
depletion . The denitrification that occurred
in the northern polar regions affected a smaller area than is generally
observed in the Southern
Hemisphere; the columns in the 65–70∘ N eqlat band in particular do
not show a significant decrease.
Figure , which consists of the time series of the zonally
averaged distribution of HNO3 retrieved total columns, illustrates all of
these features particularly well: it highlights the low and constant columns
between -40 and 40 degrees of latitude, the marked annual cycle at mid to
high latitudes and the systematic and occasional (2011, 2014, 2016) loss
of HNO3 during denitrification periods in the high latitudes of the
Southern and Northern hemispheres respectively, which are highlighted by the
iso-contours of potential vorticity at
±10 × 10-6 K m2 kg-1 s-1 (dark blue).
In order to give further insights into the interannual variability of HNO3
in polar regions, Fig. shows the seasonal cycle for each
individual year from 2008 to 2016 for eqlat 70–90 in the northern (Fig. 4a)
and the southern (Fig. 4b) hemispheres. July and August of 2010 stand out in
the Antarctic, with high and variable columns recorded by IASI. This is a
consequence of a mid-winter (mid-July) minor sudden stratospheric warming
(SSW) event, which induced a downward motion of air masses and modified the
chemical composition of the atmosphere between 10 and 50 hPa until at least
September . The principal effect of this
sudden stratospheric warming was to reduce the formation of PSCs (which
stayed well below the 1979–2012 average ) and hence reduce
denitrification. This is shown by an initial drop in HNO3 columns in June,
as is usually observed in other years but then by an increase in HNO3
columns when the SSW occurs. These results confirm those previously obtained
by the Aura MLS during that particular winter and reported in the World
Meteorological Organization (WMO) Ozone Assessment of 2014 (see Fig. 6-3 in
). Apart from these peculiarities for the year 2010, all
years seem to coincide quite well in terms of seasonality in the Southern
Hemisphere (bottom panel, Fig. 4). The timing of the HNO3 steep decrease
in particular is consistent from one year to another.
Proxies used for the regressions and their source.
ProxyDescriptionSourceSFSolar flux at 10.7 cmNOAA National Center for Environmental Information(https://www.ngdc.noaa.gov/stp/solar/flux.html)QBOQuasi-biennial oscillationFree University of Berlinindex at 10 and 30 hPa(http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html)MEIMultivariate ENSO IndexNOAA Earth System Research Laboratory(http://www.esrl.noaa.gov/psd/data/climateindices/)VPSCVolume of nitric acid trihydratesIngo Wohltmann at AWIformed in the stratosphere(personal communication, 2017)AO & AAOArctic & AntarcticNOAA Earth System Research Laboratoryoscillation indices(http://www.esrl.noaa.gov/psd/data/climateindices/)
The Northern Hemisphere high latitudes (top panel, Fig. 4) show more interannual variability than in
the south, especially during the winter because of the unusual
denitrification periods observed in 2011 (purple), 2014 (blue) and 2016
(black) in January (concentrations as low as
2.2 × 1016 molec cm-2 in 2016). In contrast to the
winter, the summer columns are more uniform from one year to another with
values around 2.1 × 1016 to
2.8 × 1016 molec cm-2.
Fitting the observations with a regression modelMulti-variable linear regression
In order to identify the processes responsible for the HNO3 variability
observed in the IASI measurements, we use a multivariate linear regression
model featuring various dynamical and chemical processes known to affect
HNO3 distributions. We strictly follow the methodology used by for
investigating O3 variability; in particular, we use daily median HNO3
total columns. These are fitted with the following model:
HNO3(t)=cst+y1⋅trend+a1⋅cos(ωt)+b1⋅sin(ωt)+∑i=2myi⋅YNorm,i(t)+ϵ(t),
where t is the day in the time series, cst is a constant term, the
y terms are the regression coefficients for each variable, ω=2π/365.25, and YNorm,i(t) refers to the chosen explanatory
variables Y, which are normalized over the period of IASI observations
(2008–2016) following
YNorm,i(t)=2(Y(t)-Ymedian)/(Ymax-Ymin),
with Ymax and Ymin being the maximum and minimum
values of the variable time series (before subtraction of the median,
Ymedian). The terms a1 and b1 in Eq. (1) are the
coefficients accounting for the annual variability in the atmosphere. They
represent mainly the seasonality
of the solar insolation and of the meridional Brewer–Dobson circulation,
which is a slow stratospheric circulation redistributing the tropical air
masses to extra-tropical regions
.
The regression coefficients are estimated by the least squares method. The
standard error (σe) of each proxy is calculated based on the
regression coefficients and is corrected in order to take the autocorrelation
uncertainty into account :
σe2=(YTY)-1⋅∑[HNO3-Yy]2n-m⋅1+φ1-φ,
where Y is the matrix of explanatory variables of size n×m, n is the number of daily measurements and m the number of fitted
parameters. HNO3 is the nitric acid column, y the vector
of regression coefficients and φ is lag-1 autocorrelation of the
residuals.
Iterative selection of explanatory variables
The choice of variables included in the model is made using an iterative
elimination procedure; all variables are tested based on their importance for
the regression . At each iteration, the variable with the
largest p value (and outside the confidence interval of 95 %) is
removed, until only the variables relevant for the regression remain,
i.e. variables with a p value smaller than 0.05. This selection algorithm is
applied on each band of equivalent latitude (or grid cell, for the global
distributions shown below) and thus yields a different combination of
variables, depending on the equivalent latitude region considered.
Normalized proxies over the IASI observations period (2008–2016).
(a) Solar flux (yellow), QBO at 10 hPa (green) and QBO at 30 hPa
(orange). (b) Antarctic Oscillation (light blue), Arctic Oscillation
(dark blue) and Multivariate ENSO Index (MEI, pink). (c) VPSC proxy
in the Northern Hemisphere (light grey) and in the Southern Hemisphere (dark
grey).
Variables used for the regression
Given the strong relationship between the O3 and HNO3 chemistry and
variability and the
novelty of applying such a regression study in an HNO3 dataset, we
consider the major and well known drivers of total O3
variability here, namely a linear trend, harmonic terms for the annual
variability and geophysical proxies for the solar cycle, the QBO, the ENSO
phenomenon and the Arctic (AO) and Antarctic oscillations (AAO) for the
Northern and Southern hemispheres, respectively. Considering the short length
of the time series, however, the linear trend did not yield any significant
result and, recalling that the aim of the paper is not to derive long term
trends, this aspect will not be discussed further. In addition, a proxy for
the volume of polar stratospheric clouds is included to account for the
effect of the strong denitrification process during the polar night (cf.
Sect. ). All the proxies are shown in Fig. and
described in more details hereafter. The source for each proxy is also
provided in Table .
Solar flux (SF)
As a proxy for the solar activity we use the 10.7 cm solar flux
(F10.7), which is a radio flux that varies daily and correlates to the
number of sunspots on the solar disk
. The data set used here is the
adjusted flux that takes the changing earth–sun distance into account. The
solar cycle directly influences the partitioning between NOy (produced by
the N2O + O1D reaction) and HNO3 through the quantity of
sunlight available, and has been known to affect the dynamics and to
influence the O3 response in the lower stratosphere
e.g..
Set of variables retained by the selection algorithm for each
equivalent latitude band.
The QBO is one of the main process regulating the dynamics of the tropical
atmosphere e.g.. It is driven by vertically
propagating gravity waves, which lead to an oscillation between stratospheric
winds blowing from the east (easterlies) and west (westerlies), occurring over a mean
period of about 28–29 months
e.g..
The effect of the QBO on the distribution of chemical species is significant, especially
in equatorial regions where both a direct effect due to the changing winds
and an indirect effect via its influence on the Brewer–Dobson circulation,
affect, for example, the distribution of ozone
e.g.. Two monthly
time series of QBO at two different pressure levels (30 and 10 hPa) from
ground-based measurements in Singapore were considered in the present
study, in order to take the differences in phase and shape of
the QBO signal in the upper and lower stratosphere into account.
Multivariate ENSO Index (MEI)
The Multivariate ENSO Index is a metric that quantifies the strength of the
El Niño–Southern Oscillation; it is computed based on the measurement of
six variables over the tropical Pacific: sea-level pressure, zonal and
meridional winds, sea surface temperature, surface air temperature, and
cloudiness fraction
. The ENSO phenomenon, even
though it is a tropospheric process (mainly sea surface temperature
contrasts), also affects stratospheric circulation. Previous studies have
shown the impact of El Niño/La Niña oscillation on stratospheric
transport processes and the generation of Rossby waves, which in turn modulate
the strength of the polar vortex
e.g. and affect O3
in the stratosphere e.g..
Arctic Oscillation and Antarctic Oscillation
The AO and AAO are included in the regression in order to represent the
atmospheric variability observed in the Northern and Southern hemispheres,
respectively . They are constructed
from the daily geopotential height anomalies in the 20–90∘ region,
at 1000 mb (for the Northern Hemisphere) and 700 mb (for the Southern
Hemisphere). Each index (AO or
AAO) is considered only in the hemisphere it is related to, while both
indices are included for equatorial latitudes. The impact of these
oscillations on O3 distributions has been demonstrated in several studies
e.g.. We may expect a similar influence on
the HNO3 distributions, particularly because, even though they are
tropospheric features, their phase and intensity affect the atmospheric
circulation, and in particular the Brewer–Dobson Circulation, up to the
stratosphere .
Volume of polar stratospheric clouds (VPSC)
The very low temperatures recorded during the winter in the polar
stratosphere inside the vortex lead to the formation of PSCs, which are
composed of nitric acid di- or trihydrates (NAD or NAT), supercooled ternary
HNO3/ H2SO4/ H2O solutions (STS) or water ice
(H2O) e.g.. Here, we consider only the NAT
particles (HNO3⋅ (H2O)3) for the PSCs, which are
ubiquitous (and often mixed with STS)
. The other forms of PSCs are expected
to influence the variability in gas-phase HNO3 to a much lesser extent
.
IASI HNO3 total columns (red dots) for each of the equivalent latitude
bands and the associated fitted model (black curves). The residuals are in
blue, and the horizontal black line represents the zero residual line. For
each equivalent latitude band, the correlation coefficient (R) between the
observations and the model fit is given in the top left corner, and the root
mean square error (RMSE) in the top right corner.
For north (a, c, e) and south (b, d, f) equivalent
latitude bands 70–90∘: (a, b) total columns (in
1016 molec cm-2) of IASI observations (red) and the regression
fit without the VPSC proxy (black), for a subset of the time series, zooming
on denitrification periods. The correlation coefficients between the fit and
the IASI data (R) are displayed, as well as the root mean square error
(RMSE). (c, d) Same as top panels but for the fit with the VPSC
proxy. (e, f) Normalized VPSC proxy. Note the different time and
value ranges between the two hemispheres.
The proxy we use here for the NAT is the volume of air below TNAT
(195 K), which depends on nitric acid concentrations, water vapour and
pressure . The temperatures needed to compute
the quantity are based on ERA-Interim reanalyses, and the HNO3 and
H2O profiles are taken north and south of 70∘ from an MLS
climatology. The proxy is calculated with a supersaturation of HNO3 over
NAT of 10, roughly corresponding to 3 K supercooling
. It should be noted that this
proxy was not included in the regression outside of the polar regions. Inside
the polar regions (eqlat bands 70–90 north and south), it was included and
subject to the selection algorithm. Finally,
for the sake of completeness, proxies accounting for the potential vorticity
(PV) and for the Eliassen–Palm flux (EPflux) were also tested in order to
take more precise patterns of the stratospheric dynamics and the
Brewer–Dobson circulation into account. Various levels for the QBO were also
tested. However, none of these proxies lead to a significant improvement of
the residuals or the correlation coefficients, and their signal is therefore
embedded here in the harmonic terms. For these reasons, they will not be
discussed further.
Results
The results are presented in two ways: latitudinally averaged time series (eqlat bands) are used to analyse the
performances of the fit in terms of correlation coefficients and residuals,
with a focus on polar regions; the performance of the model is then analysed
in terms of global distributions (with the regression applied to every
2.5∘×2.5∘ grid cell) and the spatial distribution of
the fitted proxies is detailed.
HNO3 fits for equivalent latitude bands
For each eqlat band, the variables retained by the selection procedure (see
Sect. ) are listed in Table . Most variables
are retained everywhere, except for the solar flux which is rejected in the
polar latitudes (70–90∘ N and S). The QBO30 is also excluded in the
southern polar regions (65–90∘ S) and the MEI in the northern polar
regions (65–90∘ N). Finally, the AO and AAO are excluded in the
65–70∘ N and in the 70–90∘ S bands, respectively.
The results from the multivariate regression are presented in Fig.
for each band of equivalent latitude. The model reproduces the measurements
well, with correlation coefficients between 0.81 (in the 30–40∘ N
eqlat band) and 0.94 (in the 70–90∘ S eqlat bands). Most major
features (seasonal and interannual variabilities) are reproduced by the
regression model. The residuals range between 1.74 × 1010 and
9.44 × 1015 molec cm-2, with better results for the
30∘ N–30∘ S equivalent latitude band (Root Mean Square
Error (RMSE) of 2.39 × 1014 molec cm-2) and worse fits
for the 65–70∘ S band (RMSE of
2.41 × 1015 molec cm-2). Following the comparison
between the fits and the observational data, some features can be
highlighted:
(a) Regression coefficients (xi) and their standard
error (σe, error bars, calculated by Eq. ) for the
selected variables in each equivalent latitude band (each data point is
located in the middle of its corresponding eqlat band).
(b, c) Fitted signal of the proxies in the eqlat bands 70–90
north (b) and south (c) for the variables selected. They
are calculated as [xi⋅Xi] with Xi the normalized
proxy and xi the regression coefficient calculated by the regression
model.
(a) Fraction (%) of the HNO3 variability in the IASI
observations explained by the regression model, and calculated as
σ(HNO3fit)/σ(HNO3IASI)×100. (b) Root Mean Square Error (RMSE) calculated for
each grid cell as
∑(fit-IASI)2n and expressed
in %.
The high daily variability recorded in the data during the winter for both
polar regions is not captured very well by the regression fit. Indeed, we
find that the residuals are largest in this period, especially in the
Southern Hemisphere during the denitrification period of each year (from June
until September approximately), mostly because of the high variability of the
vortex itself. We
find an average standard deviation
of 1.44 × 1016 molec cm-2 (average of the standard
deviation during the denitrification periods over the 9 years of
observation), as opposed to a mean standard deviation of
8.30 × 1015 molec cm-2 for the periods between the
denitrification seasons. In the Northern Hemisphere, the day-to-day
variability is largest during winter as well, due to the vortex building up,
and this causes larger residuals for
the corresponding months (see December through March of each year, top left
panel of Fig. , with an average standard deviation of
7.97 × 1015 molec cm-2 compared to
7.26 × 1015 molec cm-2 for other months). It is
important to stress that these larger residuals are obtained in the polar
regions despite the fact that a VPSC proxy was used. In Fig. we
show, however, that the regression model performs worse in polar regions if
the proxy is neglected, as also discussed below.
Even though the high variability during the denitrification periods is
not exactly reproduced, the amplitude of the decrease in HNO3 occurring in
the southern polar region is captured accurately by the regression model.
Figure shows a zoomed in area of Fig. to better
highlight the model performance during the denitrification periods; the
regression was tested without (top panels) and with (middle panels) the VPSC
proxy, for the 70–90∘ N (left panels) and the 70–90∘ S
(right panels) eqlat bands. The steep slope observed at the start of the low
temperatures is captured by the model when the proxy for the VPSC is included
(Fig. ) and the correlation coefficients are improved for both
hemispheres (from 0.83 to 0.86 in the 70–90∘ N and from 0.84 to
0.94 in the 70–90∘ S eqlat band). In the 65–70∘ S eqlat
band however, as previously described in Sect. , the HNO3
columns continue to increase after the formation of PSCs has started in the
70–90∘ S eqlat band. This translates to a lag between the
observations in the 65–70∘ S eqlat band and the fit, in which the
drop of HNO3 concentrations occurs earlier than in the IASI observations.
This is explained by the fact that the VPSC proxy is based on temperatures
and composition
poleward of 70∘. It induces a lower correlation
coefficient (0.87) and higher RMSE
(2.41 × 1015 molec cm-2). A proxy adapted to this eqlat
band should be used in further studies in order to represent the conditions
in this particular region of the vortex.
The high maxima seen in the IASI time series, mostly from mid-April through to
the end of May in the Southern Hemisphere, and from mid-December through
early February in the Northern Hemisphere, are not that well reproduced by
the regression model. In fact, the model fails to capture the highest columns
during the winters of each hemisphere. In the same way, a few pronounced lows
recorded by IASI, especially those in the Northern polar regions (mid-June to
early October 2014 and 2016, for instance) are not captured by the model.
Time evolution of IASI HNO3 (red) and GOME-2 NO2 (green) from
2008 to 2015 for Africa south of the Equator (5–20∘ S,
10–40∘ E). Both HNO3 and NO2 columns are expressed in
molec cm-2. The NO2 data are tropospheric columns
and are obtained from ftp://atmos.caf.dlr.de/. Note that the ranges
differ between the two y axes.
Figure shows the regression coefficients of each
variable in each equivalent latitude band (top panel). The two bottom panels
show the signal of the fitted proxies, calculated by multiplying the proxy by
its regression coefficient. Only the variables retained by the selection
algorithm are shown and discussed. From Fig. a, it can
be seen that all proxies are significant, with errors smaller than the
coefficients for all eqlat bands. It is clear that annual variability is
predominant at all latitudes. From the two bottom panels, we also see the
large influence of the VPSC in the regression for the polar regions. Their
signal is, as expected, larger in the Southern Hemisphere where it reaches
-1.3× 1016 molec cm-2, which can be compared to
maximum values of around -0.4× 1016 molec cm-2 in the
Northern Hemisphere. A noteworthy difference is found for the year 2016 where
the VPSC signal reached -0.7× 1016 molec cm-2 during
the exceptionally cold Arctic winter. While the PSCs have significantly
affected HNO3 distributions in the winters of 2011, 2014 and 2016 in the
Arctic, their influence during other years may contribute to the high
variability recorded in the observations (see first highlighted feature
above). Other proxies show relatively large signals and their global
distribution will be discussed further in Sect. .
Global model assessment with regard to the HNO3 variability
To assess the model's ability to reproduce the measurements,
Fig. a shows the percentage of the HNO3 variability seen
by IASI that is explained by the regression model. The fraction is calculated
as the difference between the standard deviation of the fit and the
observations σ(HNO3fit(t))/σ(HNO3IASI(t))×100 and is expressed as a
percentage. We find that much of the observed variability can be explained by
the model in the Southern Hemisphere (generally between 50 and 80 %). The
southern mid-latitudes and the polar regions are particularly well modelled
(70–80 %), except in Antarctica above the ice shelves. The Northern
Hemisphere HNO3 variability is reasonably well explained by the model,
particularly above 40∘ of latitude, with percentages ranging between
50 and 80 %, although some continental areas (Northern part of inner
Eurasia above Kazakhstan and the West Siberian plains) stand out with
percentages below 40 %. The region with the largest unexplained fraction
of variability is the intertropical band extending as far as 40∘
north. There, the fraction of HNO3 variability explained by the model
reaches values as low as 20 %. These regions of low explained variability
coincide quite well with the regions where high lightning activity is found,
which produces large amounts of NOx in the troposphere
. While the IASI instrument is
usually not sensitive to tropospheric HNO3, it was found that large
amounts of tropospheric HNO3 in the tropics could be detected. This is
mainly owing to the lower contribution of the stratosphere in this region,
and because the NOx produced by lightning is released in the high
troposphere, where IASI has still reasonable sensitivity. This could
consequently explain why the model is missing some of the variability
recorded in the observational data. Another cause for the discrepancies
between the observations and the model could be unaccounted sinks of HNO3,
such as deposition in the liquid or solid phase and scavenging by rain. It
should be noted that a small area of high explained variability is observed
in Africa, just south of the Equator. The variability in this region is
unexpectedly high in the IASI time series (Fig. ) and we suggest
that it could be influenced by biomass burning emissions of NO2, and
subsequent oxidation to HNO3 with a delay of about 2 months
(Fig. ) . Indeed, the
large vegetation fires in Africa every year around July emit the largest
amounts of NOx (compared to large fires in South America, Australia and
southeast Asia). Their
influence translates to an over-representation of the annual term (up to
-2× 1015 molec cm-2) in the fitted model (although
not clearly visible in Fig. because of the colour scale
chosen). This larger contribution of the annual variability thus yields a
better agreement between the observations and the model in the tropical band.
However, some of the interannual variability is missing due to the
above-mentioned fires.
Fig. b depicts the global distribution
of the RMSE of the regression expressed as a percentage. The errors are small
everywhere (between 10 and 20 %) except in the Southern Hemisphere above
Antarctica, and particularly above the ice shelves (mainly the Ross and Ronne
ice shelves). We also find higher values above large desert areas (the
Sahara, the Arabian, the Turkistan and the Australian deserts) as well as off
the west coasts of South Africa and South America where persistent low clouds
occur. Regions of low clouds or those characterized by emissivity features
that are sharp (e.g. deserts) or seasonally varying (e.g. ice shelves) are
known to cause problems for the retrieval of HNO3 using the IASI spectra
.
Global distributions (2.5∘×2.5∘ grid) of the
regression coefficients expressed in 1015 molec cm-2. The gray
crosses are the cells where the proxy is not significant when accounting for
autocorrelation (see Eq. ). The white cells are where the proxy was
not retained and the black cells represent a coefficient of 0. Note the
different scales. The x axes are latitudes.
Global patterns of fitted parameters
Figure shows the global distributions of the regression
coefficients obtained after the multivariate regression, expressed in
molec cm-2. All the variables are shown, with the areas where the proxy
was not retained left blank. The contribution of each proxy to the HNO3
variability was also calculated for each grid cell as σXi/σHNO3IASI×100 with Xi referring to each of the i explanatory variables
X, and expressed as a percentage. Note that, although the distributions of the
contribution of each proxy are not shown as a figure, the calculated
percentage values are used in the following discussion (next 3 subsections)
to quantify the influence of the fitted parameters.
The annual cycle
The annual cycle, represented by the terms a1 and b1, shows
large regression coefficients (Fig. ) and holds the
largest part of the variability globally (up to 70 % in the northern and
southern mid to high latitudes), as was previously evidenced in
Fig. a. While the Brewer–Dobson circulation,
which is embedded in these harmonic terms, influences the
HNO3 variability to some extent (through its influence on the conversion of N2O to
NOy in the tropics and through the transport of NOy-rich air masses
towards the polar regions and subsequent transformation into HNO3), the
impact of the seasonality of the solar insolation is also likely to
largely influence the annual seasonality, especially in the mid- to high
latitudes. The increasing columns recorded during the winter in both polar
regions can be explained by the combination of three processes: first, at low
temperatures, HNO3 is formed by heterogeneous reactions between N2O5
and H2Oaerosol and between ClONO2 and H2Oaerosol
or HClaerosol, which add to the main source gas-phase reaction
OH + NO2+ M → HNO3; second, while the source
reactions of HNO3 are still active, the loss reactions (HNO3 photolysis
and its reaction with OH) are significantly slowed down during the winter
; and third, as is mentioned in
Sect. , with the decrease of temperatures in the polar
stratosphere, the winds inside the polar vortex gain intensity and induce a
strong diabatic downward motion of air with little latitudinal mixing across
the vortex boundary. This descending air from the upper stratosphere enriches
the lower stratosphere in HNO3.
The solar cycle, MEI, AO/AAO and QBO
The solar flux, ENSO index and Arctic and Antarctic Oscillation
(Fig. ) all have a similar influence in terms of magnitude
(between -2.5× 1015 and
2.5 × 1015 molec cm-2), although with different spatial
patterns. The influence of the solar flux is positive in the northern polar
latitudes and in the tropical and southern mid-latitudes. It is close to zero
or negative elsewhere. While previous studies showed a positive signal
globally in the low stratosphere for the response of O3 to the solar cycle
, our results for the mid to high northern
latitudes suggest opposite behaviour (negative signal) for HNO3. However,
the positive contribution of the solar cycle on the HNO3 variation in the
tropical and southern mid-latitude stratosphere is in line with the O3
response previously reported
. Note also that
the strong negative signal observed above the ice shelves of western
Antarctica is most probably due to the drawback of using a
constant emissivity for ocean surfaces for all seasons (e.g. even when the ocean becomes
frozen). For this reason, the regression coefficients in this area will not
be discussed further.
The MEI shows a negative signal above the northern polar regions and in the
eastern parts of the Pacific and Atlantic oceans (especially west of South Africa). A positive signal is observed above Australia and above the southern
polar regions. Overall, the MEI influence is quite small, which is not
surprising considering that it affects mostly the tropospheric circulation,
where IASI is less sensitive. Its signature is nonetheless visible and
significant in the eastern Pacific, where it contributes to up to 30% of
the HNO3 variability, and in the mid-latitudes of the Northern Hemisphere.
The east–west gradient is in good agreement with chemical and dynamical
effects of El Niño on O3, and with previous studies that showed the same
patterns for the influence of the MEI on O3.
The Arctic Oscillation (AO) signal is stronger, especially above the Atlantic
Ocean, with a positive signal above eastern Canada and Greenland and between
the north of eastern Africa and Florida. Except for those two regions, the AO
contributes at mid to high latitudes of the Northern Hemisphere with a
negative signal, which contributes 10–20 % to the HNO3 variability.
The corresponding proxy for the Southern Hemisphere (AAO) is also
significant, with a strong positive signal above the vortex rim and a
negative signal above Antarctica. These results are in agreement with
previous studies that showed that, for O3, both the Arctic and Antarctic
oscillations (also called “annular modes”) are leading modes of variation
in the extratropical atmosphere
. Both the AO and the AAO
strongly influence the circulation up to the lower stratosphere and
represent, particularly in the Southern Hemisphere, fluctuations in the
strength of the polar vortex
. This further shows the similarity in
the behaviour of O3 and HNO3.
The QBO has a generally small influence on the distributions with, however,
some contribution (up to 30 %) in the equatorial band as expected
. As previously mentioned, several tests were performed (not shown here) with the QBO taken at other atmospheric pressure levels
(namely 20 and 50 hPa), and similar results were
obtained. Even though the QBO is a tropical phenomenon, its effects extend as
far as the polar latitudes, through the modulation of the planetary Rossby
waves e.g.. Because there are more
topographical features in the Northern Hemisphere than in the Southern
Hemisphere, these waves have a larger amplitude and can influence the Arctic
stratospheric temperatures and hence the vortex formation. While the exact
mechanism for the extratropical influence of the QBO is not exactly
understood , it seems the large positive and
negative signals observed in the northern high latitudes in
Fig. can indeed be attributed to the modulation of the
Rossby waves by the oscillation in the meridional circulation. This was also
observed for O3 in studies such as .
VPSC
The annual cycle, which is the dominant factor for HNO3 variability at all
latitudes, leading to the build-up of concentrations during the winter, is
interrupted in the southern polar regions, particularly in the
70–90∘ S eqlat band (see also Fig. ), by the
condensation and subsequent sedimentation of PSCs. The VPSC proxy, reflecting
the volume of air below TNAT, has a strong inverse correlation with
HNO3 columns, which decrease (negative values) with increasing VPSC
e.g.. The signal of the VPSC proxy is thus,
as expected, negative everywhere (in the polar regions considered), with
values around -6× 1015 molec cm-2. When looking at
their contribution, we find that the PSCs account for a larger part of the
HNO3 variability (40–60 %) in the Southern Hemisphere, where the
influence of denitrification is indeed expected to be more important,
compared to the Northern Hemisphere (maxima of 40 %), as discussed in
Sect. with the analysis of Fig. . The small
areas with a positive signal appear to be non significant (see grey crosses).
Conclusions
Time series of HNO3 total columns retrieved from IASI/Metop between 2008
and 2016 have been presented and analysed in terms of seasonal cycle and
global variability. The analysis was conducted in terms of equivalent
latitudes (here calculated on the basis of potential vorticity) and focused
mainly on high latitude regions. We have shown that the IASI instrument
captures the broad patterns of the seasonal cycles at all latitudes but also
year-to-year specific behaviours. The systematic denitrification process
occurring every winter–spring in the Southern Hemisphere shows up
unambiguously in the time evolutions and the use of equivalent latitudes
enables one to isolate the regions affected based on the dominating stratospheric
dynamical regimes. Three distinct zones within the polar regions were separated in
particular: (1) the inner polar region (70–90∘ S), where
the denitrification starts earliest and where the HNO3 columns reach
their lowest values for the longest period; (2) the outer part of the polar
vortex (65–70∘ S), where the HNO3 columns drop occurs 1 month
later and the minimum concentrations do not reach such low levels; (3) the
polar vortex edges (55–65∘ S), where the columns follow a more
normal annual cycle, with maxima around July, forming a collar of high
columns around the denitrified vortex. The IASI-derived HNO3 distributions
also reflect the denitrification periods in the Northern Hemisphere, during
the exceptionally cold winters of 2011, 2014 and 2016.
The HNO3 time series were successfully fitted with multivariate
regressions in order to identify the various factors responsible for the
variability in the observations. To the best of our knowledge, this is the
first time that such regression models have been applied to HNO3 time
evolution. A specific set of explanatory variables was retained for each
equivalent latitude band following an iterative procedure, according to the
influence of each of these variables in the regression. The regression model
allowed good representation of the IASI observations in most cases
(correlation coefficients between 0.81 and 0.94). However, the variability
recorded in the tropics could not be reproduced that well, with only about 20
to 40 % correctly accounted for. The regression for other parts of the
globe yielded better results, especially in the southern polar regions, where
a high percentage (60–80 %) of the observed variability is reproduced by
the regression. Generally, it was found that the annual cycle is the factor
responsible for the largest part of the variability, showing a hemispheric
pattern. The Brewer–Dobson circulation, and also the solar insolation
seasonality, which are embedded in the harmonic terms, seem to be the main
drivers of variability; the
Brewer–Dobson circulation carries NOy towards the poles and both
processes bring HNO3 concentrations to their maxima during the local
winter when production is enhanced and destruction inhibited. We also
interestingly show that polar stratospheric clouds are the second most
important driver of the variability of HNO3 in the southern polar
latitudes (65–90∘ S). The influence of PSCs is, as expected, less
marked in the Northern Hemisphere, but accounting for PSCs still
significantly improves the model-to-observation agreement especially during
the colder northern winters (R from 0.83 to 0.86). While we feel that the
VPSC proxy used here for the PSCs (including only the NAT) is generally good,
it is not excluded that adding other forms of PSCs would further improve the
model. In any case, the present work shows the potential of using IASI
measurements to study the polar denitrification processes in depth.
In the mid- and tropical latitudes, the annual cycle is still
prominent, but the relative influence of the QBO increases. Most of the weak
seasonality revealed by IASI in the tropical regions is explained by the
annual cycle (as well as a potential contribution of African fires and
lightning as additional NOx sources), the QBO and the MEI.
More generally, this study shows that the IASI data allow a good analysis and
understanding of the HNO3 variability in the atmosphere. The measurements
are made with exceptional spatial and temporal sampling, which allows a
detailed analysis of the polar regions throughout the entire year. The amount
of data allows for a thorough monitoring of the processes regulating the
HNO3 variability, such as denitrification processes in the southern
polar regions, or seasonal variability in tropical regions. The IASI
HNO3 time series will soon be extended with the launch of Metop C in
September 2018, which should further improve the regression model. As shown
here by the still significant residuals at some periods and locations, other
factors could also probably be included to acquire a full and coherent
representation of the HNO3 total columns variability.
The IASI L2 data are available upon request to the
corresponding author.
The authors declare that they have no conflict of
interest.
Acknowledgements
IASI was developed and built under the responsibility of the “Centre
National d'Etudes Spatiales” (CNES, France). It is flown on board the Metop
satellites as part of the EUMETSAT Polar System. The research was funded by
the F.R.S.-FNRS, the Belgian State Federal Office for Scientific, Technical
and Cultural Affairs (Prodex arrangement 4000111403 IASI.FLOW) and EUMETSAT
through the Satellite Application Facility on Atmospheric Composition
Monitoring (ACSAF). The authors would like to thank Ingo Wohltmann for the
VPSC proxy and for useful discussions. Gaétane Ronsmans is grateful to
the “Fonds pour la Formation à la Recherche dans l'Industrie et dans
l'Agriculture” of Belgium for a PhD grant (Boursier FRIA). Cathy Clerbaux is
grateful to CNES for financial support. Edited
by: Jianzhong Ma Reviewed by: Michelle Santee and one
anonymous referee
ReferencesAustin, J., Garcia, R. R., Russell, J. M., Solomon, S., and Tuck, A. F.: On
the Atmospheric Photochemistry of Nitric Acid, J. Geophys. Res., 91,
5477–5485, 10.1029/JD091iD05p05477, 1986.Austin, J., Hood, L. L., and Soukharev, B. E.: Solar cycle variations of
stratospheric ozone and temperature in simulations of a coupled
chemistry-climate model, Atmos. Chem. Phys., 7, 1693–1706,
10.5194/acp-7-1693-2007, 2007.Baldwin, M. P., Gray, L. J., Dunkerton, T. J., Hamilton, K., Haynes, P. H.,
Holton, J. R., Alexander, M. J., Hirota, I., Horinouchi, T., Jones, D. B. A.,
Marquardt, C., Sato, K., and Takahashi, M.: The quasi-biennial oscillation,
Rev. Geophys., 39, 179–229, 10.1029/1999RG000073, 2001.Barbosa, P. M., Stroppiana, D., Grégoire, J., and Pereira, J. M. C.:
An assessment of vegetation fire in Africa (1981–1991): Burned areas,
burned biomass, and atmospheric emissions, Global Biogeochem. Cy., 13,
933–950, 10.1029/1999GB900042, 1999.Butchart, N.: The Brewer–Dobson circulation, Rev. Geophys, 52, 157–184,
10.1002/2013RG000448, 2014.Chehade, W., Weber, M., and Burrows, J. P.: Total ozone trends and
variability during 1979–2012 from merged data sets of various satellites,
Atmos. Chem. Phys., 14, 7059–7074, 10.5194/acp-14-7059-2014,
2014.Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J.,
Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C.,
and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal
infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, 6041–6054,
10.5194/acp-9-6041-2009, 2009Cooper, M., Martin, R. V., Wespes, C., Coheur, P.-F., Clerbaux, C., and
Murray, L. T.: Tropospheric nitric acid columns from the IASI satellite
instrument interpreted with a chemical transport model: Implications for
parametrizations of nitric oxide production by lightning, J. Geophys. Res.,
119, 68–79, 10.1002/2014JD021907, 2014.Covington, A. E.: Solar Noise Observations on 10.7 Centimeters, P. IRE, 36,
454–457, 10.1109/JRPROC.1948.234598, 1948.de Laat, A. T. J. and van Weele, M.: The 2010 Antarctic ozone hole: Observed
reduction in ozone destruction by minor sudden stratospheric warmings, Sci.
Rep.-UK, 1, 38, 10.1038/srep00038, 2011.de Laat, A. T. J., van der A, R. J., and van Weele, M.: Tracing the second
stage of ozone recovery in the Antarctic ozone-hole with a “big data”
approach to multivariate regressions, Atmos. Chem. Phys., 15, 79–97,
10.5194/acp-15-79-2015, 2015.Drdla, K. and Müller, R.: Temperature thresholds for polar stratospheric
ozone loss, Atmos. Chem. Phys. Discuss., 10, 28687–28720,
10.5194/acpd-10-28687-2010, 2010.Finlayson-Pitts, B. J. and Pitts, J. N.: Chemistry of the Upper and Lower
Atmosphere, Elsevier, 957–969, 10.1016/B978-012257060-5/50025-3, 2000.Fischer, H., Waibel, A. E., Welling, M., Wienhold, F. G., Zenker, T.,
Crutzen, P. J., Arnold, F., Bürger, V., Schneider, J., Bregman, A.,
Lelieveld, J., and Siegmund, P. C.: Observations of high concentration of
total reactive nitrogen (NOy) and nitric acid (HNO3) in the lower
Arctic stratosphere during the Stratosphere-Troposphere Experiment by
Aircraft Measurements (STREAM) II campaign in February 1995, J. Geophys.
Res., 102, 23559, 10.1029/97JD02012, 1997.Frossard, L., Rieder, H. E., Ribatet, M., Staehelin, J., Maeder, J. A., Di
Rocco, S., Davison, A. C., and Peter, T.: On the relationship between total
ozone and atmospheric dynamics and chemistry at mid-latitudes – Part 1:
Statistical models and spatial fingerprints of atmospheric dynamics and
chemistry, Atmos. Chem. Phys., 13, 147–164,
10.5194/acp-13-147-2013, 2013.Garfinkel, C. I., Shaw, T. A., Hartmann, D. L., and Waugh, D. W.: Does the
Holton–Tan Mechanism Explain How the Quasi-Biennial Oscillation Modulates
the Arctic Polar Vortex?, J. Atmos. Sci., 69, 1713–1733,
10.1175/JAS-D-11-0209.1, 2012.Garfinkel, C. I., Hurwitz, M. M., and Oman, L. D.: Effect of recent sea
surface temperature trends on the Arctic stratospheric vortex, J. Geophys.
Res.-Atmos., 120, 5404–5416, 10.1002/2015JD023284, 2015.Gobbi, G. P., Deshler, T., Adriani, A., and Hofmann, D. J.: Evidence for
denitrification in the 1990 Antarctic spring stratosphere: I, Lidar and
temperature measurements, Geophys. Res. Lett., 18, 1995–1998,
10.1029/91GL02310, 1991.Gong, D. and Wang, S.: Definition of Antarctic Oscillation index, Geophys.
Res. Lett., 26, 459–462, 10.1029/1999GL900003, 1999.Hanson, D. and Mauersberger, K.: Laboratory studies of the nitric acid
trihydrate: Implications for the south polar stratosphere, Geophys. Res.
Lett., 15, 855–858, 10.1029/GL015i008p00855, 1988.Harris, N. R. P., Lehmann, R., Rex, M., and von der Gathen, P.: A closer look
at Arctic ozone loss and polar stratospheric clouds, Atmos. Chem. Phys., 10,
8499–8510, 10.5194/acp-10-8499-2010, 2010.Hauchecorne, A., Bertaux, J. L., Dalaudier, F., Keckhut, P., Lemennais, P.,
Bekki, S., Marchand, M., Lebrun, J. C., Kyrölä, E., Tamminen, J.,
Sofieva, V., Fussen, D., Vanhellemont, F., Fanton d'Andon, O., Barrot, G.,
Blanot, L., Fehr, T., and Saavedra de Miguel, L.: Response of tropical
stratospheric O3, NO2 and NO3 to the equatorial Quasi-Biennial
Oscillation and to temperature as seen from GOMOS/ENVISAT, Atmos. Chem.
Phys., 10, 8873–8879, 10.5194/acp-10-8873-2010, 2010.Hilton, F., Armante, R., August, T., Barnet, C., Bouchard, A., Camy-Peyret,
C., Capelle, V., Clarisse, L., Clerbaux, C., Coheur, P.-F., Collard, A.,
Crevoisier, C., Dufour, G., Edwards, D., Faijan, F., Fourrié, N.,
Gambacorta, A., Goldberg, M., Guidard, V., Hurtmans, D., Illingworth, S.,
Jacquinet-Husson, N., Kerzenmacher, T., Klaes, D., Lavanant, L., Masiello,
G., Matricardi, M., McNally, A., Newman, S., Pavelin, E., Payan, S.,
Péquignot, E., Peyridieu, S., Phulpin, T., Remedios, J.,
Schlüssel, P., Serio, C., Strow, L., Stubenrauch, C., Taylor, J.,
Tobin, D., Wolf, W., and Zhou, D.: Hyperspectral Earth Observation from
IASI: Five Years of Accomplishments, B. Am. Meteorol. Soc., 93, 347–370,
10.1175/BAMS-D-11-00027.1, 2012.Holton, J. R. and Tan, H.-C.: The Influence of the Equatorial Quasi-Biennial
Oscillation on the Global Circulation at 50 mb, J. Atmos. Sci., 37,
2200–2208, 10.1175/1520-0469(1980)037<2200:TIOTEQ>2.0.CO;2, 1980.Hood, L. L.: The solar cycle variation of total ozone: Dynamical forcing in
the lower stratosphere, J. Geophys. Res., 102, 1355–1370,
10.1029/2007JD009391, 1997.Hood, L. L. and Soukharev, B. E.: Quasi-Decadal Variability of the Tropical
Lower Stratosphere: The Role of Extratropical Wave Forcing, J. Atmos. Sci.,
60, 2389–2403, 10.1175/1520-0469(2003)060<2389:QVOTTL>2.0.CO;2, 2003.Hood, L. L., Soukharev, B. E., and McCormack, J. P.: Decadal variability of
the tropical stratosphere: Secondary influence of the El NiñoSouthern
Oscillation, J. Geophys. Res.-Atmos., 115, 1–16,
10.1029/2009JD012291, 2010.Hoyle, C. R., Engel, I., Luo, B. P., Pitts, M. C., Poole, L. R., Grooß,
J.-U., and Peter, T.: Heterogeneous formation of polar stratospheric clouds
– Part 1: Nucleation of nitric acid trihydrate (NAT), Atmos. Chem. Phys.,
13, 9577–9595, 10.5194/acp-13-9577-2013, 2013.Hurtmans, D., Coheur, P.-F., Wespes, C., Clarisse, L., Scharf, O., Clerbaux,
C., Hadji-Lazaro, J., George, M., and Turquety, S.: FORLI radiative transfer
and retrieval code for IASI, J. Quant. Spectrosc. Ra., 113, 1391–1408,
10.1016/j.jqsrt.2012.02.036, 2012.Jacob, D. J.: Heterogeneous chemistry and tropospheric ozone, Atmos.
Environ., 34, 2131–2159, 10.1016/S1352-2310(99)00462-8, 2000.Jones, J. M. and Widmann, M.: Early peak in Antarctic oscillation index,
Nature, 432, 290–291, 10.1038/432290b, 2004.Khosrawi, F., Urban, J., Lossow, S., Stiller, G., Weigel, K., Braesicke, P.,
Pitts, M. C., Rozanov, A., Burrows, J. P., and Murtagh, D.: Sensitivity of
polar stratospheric cloud formation to changes in water vapour and
temperature, Atmos. Chem. Phys., 16, 101–121,
10.5194/acp-16-101-2016, 2016.Kirner, O., Ruhnke, R., Buchholz-Dietsch, J., Jöckel, P., Brühl, C.,
and Steil, B.: Simulation of polar stratospheric clouds in the
chemistry-climate-model EMAC via the submodel PSC, Geosci. Model Dev., 4,
169–182, 10.5194/gmd-4-169-2011, 2011.Kirner, O., Müller, R., Ruhnke, R., and Fischer, H.: Contribution of
liquid, NAT and ice particles to chlorine activation and ozone depletion in
Antarctic winter and spring, Atmos. Chem. Phys., 15, 2019–2030,
10.5194/acp-15-2019-2015, 2015.Klekociuk, A., Tully, M., Alexander, S., Dargaville, R., Deschamps, L.,
Fraser, P., Gies, H., Henderson, S., Javorniczky, J., Krummel, P., Petelina,
S., Shanklin, J., Siddaway, J., and Stone, K.: The Antarctic ozone hole
during 2010, Aust. Meteorol. Ocean, 61, 253–267, 10.22499/2.6104.006,
2011.Knibbe, J. S., van der A, R. J., and de Laat, A. T. J.: Spatial regression
analysis on 32 years of total column ozone data, Atmos. Chem. Phys., 14,
8461–8482, 10.5194/acp-14-8461-2014, 2014.Kodera, K. and Kuroda, Y.: Tropospheric and stratospheric aspects of the
Arctic Oscillation, Geophys. Res. Lett., 27, 3349–3352,
10.1029/2000GL012017, 2000.Kodera, K. and Kuroda, Y.: Dynamical response to the solar cycle, J.
Geophys. Res.-Atmos., 107, 1–12, 10.1029/2002JD002224, 2002.Konopka, P., Ploeger, F., Tao, M., Birner, T., and Riese, M.: Hemispheric
asymmetries and seasonality of mean age of air in the lower stratosphere:
Deep versus shallow branch of the Brewer–Dobson circulation, J. Geophys.
Res.-Atmos., 120, 2053–2066, 10.1002/2014JD022429, 2015.Labrador, L. J., Von Kuhlmann, R., and Lawrence, M. G.: Strong sensitivity
of the global mean OH concentration and the tropospheric oxidizing efficiency
to the source of NOx from lightning, Geophys. Res. Lett., 31, L06102,
10.1029/2003GL019229, 2004.Lambert, A., Santee, M. L., and Livesey, N. J.: Interannual variations of
early winter Antarctic polar stratospheric cloud formation and nitric acid
observed by CALIOP and MLS, Atmospheric Chemistry and Physics, 16,
15 219–15 246, 10.5194/acp-16-15219-2016, 2016.Lary, D. J.: Catalytic destruction of stratospheric ozone, J. Geophys.
Res., 102, 21515–21526, 10.1029/97JD00912, 1997.Lee, A. M., Roscoe, H. K., Jones, A. E., Haynes, P. H., Shuckburgh, E. F.,
Morrey, M. W., and Pumphrey, H. C.: The impact of the mixing properties
within the Antarctic stratospheric vortex on ozone loss in spring, J.
Geophys. Res., 106, 3203–3211, 10.1029/2000JD900398, 2001.Lee, H. and Smith, A. K.: Simulation of the combined effects of solar cycle,
quasi-biennial oscillation, and volcanic forcing on stratospheric ozone
changes in recent decades, J. Geophys. Res., 108, 1–16,
10.1029/2001JD001503, 2003.Lee, S., Shelow, D. M., Thompson, A. M., and Miller, S. K.: QBO and ENSO
variability in temperature and ozone from SHADOZ, 1998–2005, J. Geophys.
Res.-Atmos., 115, 1998–2005, 10.1029/2009JD013320, 2010.Lowe, D. and MacKenzie, A. R.: Polar stratospheric cloud microphysics and
chemistry, J. Atmos. Ocean. Tech., 70, 13–40,
10.1016/j.jastp.2007.09.011, 2008.Mäder, J. A., Staehelin, J., Brunner, D., Stahel, W. A., Wohltmann, I.,
and Peter, T.: Statistical modeling of total ozone: Selection of appropriate
explanatory variables, J. Geophys. Res., 112, D11108,
10.1029/2006JD007694, 2007.Mäder, J. A., Staehelin, J., Peter, T., Brunner, D., Rieder, H. E., and
Stahel, W. A.: Evidence for the effectiveness of the Montreal Protocol to
protect the ozone layer, Atmos. Chem. Phys., 10, 12161–12171,
10.5194/acp-10-12161-2010, 2010.Manney, G. L. and Lawrence, Z. D.: The major stratospheric final warming in
2016: dispersal of vortex air and termination of Arctic chemical ozone loss,
Atmos. Chem. Phys., 16, 15371–15396,
10.5194/acp-16-15371-2016, 2016.Manney, G. L., Zurek, R., O'Neill, A., and Swinbank, R.: On the motion of
air through the stratospheric polar vortex, J. Atmos. Sci., 51, 2973–2994,
10.1175/1520-0469(1994)051<2973:OTMOAT>2.0.CO;2, 1994.Manney, G. L., Michelsen, H. A., Santee, M. L., Gunson, M. R., Irion, F. W.,
Roche, A. E., and Livesey, N. J.: Polar vortex dynamics during spring and
fall diagnosed using trace gas observations from the Atmospheric Trace
Molecule Spectroscopy instrument, J. Geophys. Res.-Atmos., 104,
18841–18866, 10.1029/1999JD900317, 1999.Manney, G. L., Santee, M. L., Rex, M., Livesey, N. J., Pitts, M. C.,
Veefkind, P., Nash, E. R., Wohltmann, I., Lehmann, R., Froidevaux, L., Poole,
L. R., Schoeberl, M. R., Haffner, D. P., Davies, J., Dorokhov, V., Gernandt,
H., Johnson, B., Kivi, R., Kyrö, E., Larsen, N., Levelt, P. F.,
Makshtas, A., McElroy, C. T., Nakajima, H., Parrondo, M. C., Tarasick, D. W.,
von der Gathen, P., Walker, K. A., and Zinoviev, N. S.: Unprecedented Arctic
ozone loss in 2011, Nature, 478, 469–475, 10.1038/nature10556, 2011.Matthias, V., Dörnbrack, A., and Stober, G.: The extraordinarily
strong and cold polar vortex in the early northern winter 2015/2016,
Geophys. Res. Lett., 43, 12287–12294, 10.1002/2016GL071676, 2016.Maycock, A. C., Matthes, K., Tegtmeier, S., Thiéblemont, R., and Hood,
L.: The representation of solar cycle signals in stratospheric ozone – Part
1: A comparison of recently updated satellite observations, Atmos. Chem.
Phys., 16, 10021–10043, 10.5194/acp-16-10021-2016, 2016.McCormack, J. P., Siskind, D. E., and Hood, L. L.: Solar-QBO interaction and
its impact on stratospheric ozone in a zonally averaged photochemical
transport model of the middle atmosphere, J. Geophys. Res.-Atmos., 112,
D16109, 10.1029/2006JD008369, 2007.McDonald, M., de Zafra, R., and Muscari, G.: Millimeter wave spectroscopic
measurements over the South Pole 5, Morphology and evolution of HNO3
vertical distribution, 1993 versus 1995, J. Geophys. Res., 105,
17739–17750, 10.1029/2000JD900120, 2000.Miller, A. J., Cai, A., Tiao, G., Wuebbles, D. J., Flynn, L. E., Yang, S. K.,
Weatherhead, E. C., Fioletov, V., Petropavlovskikh, I., Meng, X. L., Guillas,
S., Nagatani, R. M., and Reinsel, G. C.: Examination of ozonesonde data for
trends and trend changes incorporating solar and Arctic oscillation signals,
J. Geophys. Res.-Atmos., 111, D13305, 10.1029/2005JD006684, 2006.Mohanakumar, K.: Stratosphere Troposphere Interactions – An Introduction,
Springer Netherlands, 10.1007/978-1-4020-8217-7, 2008.Morgenstern, O., Braesicke, P., Hurwitz, M. M., O'Connor, F. M., Bushell,
A. C., Johnson, C. E., and Pyle, J. A.: The world avoided by the Montreal
Protocol, Geophys. Res. Lett., 35, 1–5, 10.1029/2008GL034590, 2008.Neuman, J. A., Gao, R. S., Fahey, D. W., Holecek, J. C., Ridley, B. A.,
Walega, J. G., Grahek, F. E., Richard, E. C., McElroy, C. T., Thompson,
T. L., Elkins, J. W., Moore, F. L., and Ray, E. A.: In situ measurements of
HNO3, NOy, NO, and O3 in the lower stratosphere and upper
troposphere, Atmos. Environ., 35, 5789–5797,
10.1016/S1352-2310(01)00354-5, 2001.Newman, P. A., Nash, E. R., and Rosenfield, J. E.: What controls the
temperature of the Arctic stratosphere during the spring?, J. Geophys. Res.,
106, 19999–20010, 10.1029/2000jd000061, 2001.Peter, T.: Microphysics and heterogeneous chemistry of polar stratospheric
clouds, Ann. Rev. Phys. Chem., 48, 785–822,
10.1146/annurev.physchem.48.1.785, 1997.Piccolo, C. and Dudhia, A.: Precision validation of MIPAS-Envisat products,
Atmos. Chem. Phys., 7, 1915–1923, 10.5194/acp-7-1915-2007,
2007.Pitts, M. C., Poole, L. R., and Thomason, L. W.: CALIPSO polar stratospheric
cloud observations: second-generation detection algorithm and composition
discrimination, Atmos. Chem. Phys., 9, 7577–7589,
10.5194/acp-9-7577-2009, 2009.Popp, P. J., Marcy, T. P., Gao, R. S., Watts, L. A., Fahey, D. W., Richard,
E. C., Oltmans, S. J., Santee, M. L., Livesey, N. J., Froidevaux, L., Sen,
B., Toon, G. C., Walker, K. A., Boone, C. D., and Bernath, P. F.:
Stratospheric correlation between nitric acid and ozone, J. Geophys. Res.,
114, D03305, 10.1029/2008JD010875, 2009.Randel, W. J. and Thompson, A. M.: Interannual variability and trends in
tropical ozone derived from SAGE II satellite data and SHADOZ ozonesondes,
J. Geophys. Res.-Atmos., 116, D07303, 10.1029/2010JD015195, 2011.Randel, W. J., Garcia, R. R., Calvo, N., and Marsh, D.: ENSO influence on
zonal mean temperature and ozone in the tropical lower stratosphere,
Geophys. Res. Lett., 36, L15822, 10.1029/2009GL039343, 2009.Rieder, H. E., Frossard, L., Ribatet, M., Staehelin, J., Maeder, J. A., Di
Rocco, S., Davison, A. C., Peter, T., Weihs, P., and Holawe, F.: On the
relationship between total ozone and atmospheric dynamics and chemistry at
mid-latitudes – Part 2: The effects of the El Niño/Southern Oscillation,
volcanic eruptions and contributions of atmospheric dynamics and chemistry to
long-term total ozone changes, Atmos. Chem. Phys., 13, 165–179,
10.5194/acp-13-165-2013, 2013.Rodgers, C. D.: Inverse Methods for Atmospheric Sounding – Theory and
Practice, vol. 2, Series on Atmospheric Oceanic and Planetary Physics, World
Scientific Publishing, 10.1142/9789812813718, 2000.Ronsmans, G., Langerock, B., Wespes, C., Hannigan, J. W., Hase, F.,
Kerzenmacher, T., Mahieu, E., Schneider, M., Smale, D., Hurtmans, D., De
Mazière, M., Clerbaux, C., and Coheur, P.-F.: First characterization and
validation of FORLI-HNO3 vertical profiles retrieved from IASI/Metop,
Atmos. Meas. Tech., 9, 4783–4801, 10.5194/amt-9-4783-2016,
2016.Roscoe, H. K., Feng, W., Chipperfield, M. P., Trainic, M., and Shuckburgh,
E. F.: The existence of the edge region of the Antarctic stratospheric
vortex, J. Geophys. Res.-Atmos., 117, D04301, 10.1029/2011JD015940,
2012.Santee, M. L., Manney, G. L., Froidevaux, L., Read, W. G., and Waters, J. W.:
Six years of UARS Microwave Limb Sounder HNO3 observations : Seasonal,
interhemispheric, and interannual variations in the lower stratosphere, J.
Geophys. Res., 104, 8225–8246, 10.1029/1998JD100089, 1999.Santee, M. L., Manney, G. L., Livesey, N. J., and Read, W. G.:
Three-dimensional structure and evolution of stratospheric HNO3 based on
UARS microwave limb sounder measurements, J. Geophys. Res.-Atmos., 109,
D15306, 10.1029/2004JD004578, 2004.Santee, M. L., Manney, G. L., Livesey, N. J., Froidevaux, L., MacKenzie,
I. A., Pumphrey, H. C., Read, W. G., Schwartz, M. J., Waters, J. W., and
Harwood, R. S.: Polar processing and development of the 2004 Antarctic ozone
hole: First results from MLS on Aura, Geophys. Res. Lett., 32, L12817,
10.1029/2005GL022582, 2005.Sauvage, B., Martin, R. V., van Donkelaar, A., and Ziemke, J. R.:
Quantification of the factors controlling tropical tropospheric ozone and
the South Atlantic maximum, J. Geophys. Res.-Atmos., 112, D11309,
10.1029/2006JD008008, 2007.Schirber, S.: Influence of ENSO on the QBO: Results from an ensemble of
idealized simulations, J. Geophys. Res.-Atmos., 120, 1109–1122,
10.1002/2014JD022460, 2015.Schoeberl, M. R. and Hartmann, D. L.: The Dynamics of the Stratospheric
Polar Vortex and Its Relation to Springtime Ozone Depletions, Science, 251,
46–52, 10.1126/science.251.4989.46, 1991.Scholes, R., Ward, D., and Justice, C.: Emissions of trace gases and aerosol
particles due to vegetation burning in southern hemisphere Africa, J.
Geophys. Res., 101, 23677–23682, 10.1029/95JD02049, 1996.Schreier, S. F., Richter, A., Kaiser, J. W., and Burrows, J. P.: The
empirical relationship between satellite-derived tropospheric NO2 and fire
radiative power and possible implications for fire emission rates of NOx,
Atmos. Chem. Phys., 14, 2447–2466, 10.5194/acp-14-2447-2014,
2014.Sioris, C. E., McLinden, C. A., Fioletov, V. E., Adams, C., Zawodny, J. M.,
Bourassa, A. E., Roth, C. Z., and Degenstein, D. A.: Trend and variability in
ozone in the tropical lower stratosphere over 2.5 solar cycles observed by
SAGE II and OSIRIS, Atmos. Chem. Phys., 14, 3479–3496,
10.5194/acp-14-3479-2014, 2014.Solomon, A., Richter, J. H., and Bacmeister, J. T.: An objective analysis of
the QBO in ERA-Interim and the Community Atmosphere Model, version 5,
Geophys. Res. Lett., 41, 7791–7798, 10.1002/2014GL061801, 2014.Solomon, S.: Stratospheric ozone depletion: A review of concepts and
history, Rev. Geophys., 37, 275–316, 10.1029/1999RG900008, 1999.Soukharev, B. E. and Hood, L. L.: Solar cycle variation of stratospheric
ozone: Multiple regression analysis of long-term satellite data sets and
comparisons with models, J. Geophys. Res.-Atmos., 111, D20314,
10.1029/2006JD007107, 2006.Steinbrecht, W.: Enhanced upper stratospheric ozone: Sign of recovery or
solar cycle effect?, J. Geophys. Res., 109, D02308,
10.1029/2003JD004284, 2004.Tabazadeh, A., Toon, O. B., and Jensen, E. J.: Formation and implications of
ice particle nucleation in the stratosphere, Geophys. Res. Lett., 24,
2007–2010, 10.1029/97GL01883, 1997.Tapping, K. F.: The 10.7 cm solar radio flux (F10.7), Adv. Space Res., 11,
394–406, 10.1002/swe.20064, 2013.Tapping, K. F. and DeTracey, B.: The origin of the 10.7 cm flux, Solar
Phys., 127, 321–332, 10.1007/BF00152171, 1990.Thompson, D. W. J. and Wallace, J. M.: Annular Modes in the Extratropical
Circulation – Part I : Month-to-Month Variability, J. Climate, 13,
1000–1016, 10.1175/1520-0442(2000)01360;1000:amitec62;2.0.co;2, 2000.Toon, G. C., Farmer, C. B., Lowes, L. L., Schaper, P. W., Blavier, J., and
Norton, R. H.: Infrared Aircraft Measurements of Stratospheric Composition
Over Antarctica During September 1987, J. Geophys. Res., 94, 16571–16596,
10.1029/JD094iD14p16571, 1989.Trenberth, K. E., Branstator, G. W., Karoly, D., Kumar, A., Lau, N.-C., and
Ropelewski, C.: Progress during TOGA in understanding and modeling global
teleconnections associated with tropical sea surface temperatures, J.
Geophys. Res.-Oceans, 103, 14291–14324, 10.1029/97JC01444, 1998.Urban, J., Pommier, M., Murtagh, D. P., Santee, M. L., and Orsolini, Y. J.:
Nitric acid in the stratosphere based on Odin observations from 2001 to 2009
– Part 1: A global climatology, Atmos. Chem. Phys., 9, 7031–7044,
10.5194/acp-9-7031-2009, 2009.Valks, P., Pinardi, G., Richter, A., Lambert, J.-C., Hao, N., Loyola, D., Van
Roozendael, M., and Emmadi, S.: Operational total and tropospheric NO2
column retrieval for GOME-2, Atmos. Meas. Tech., 4, 1491–1514,
10.5194/amt-4-1491-2011, 2011.Van Damme, M., Whitburn, S., Clarisse, L., Clerbaux, C., Hurtmans, D., and
Coheur, P.-F.: Version 2 of the IASI NH3 neural network retrieval algorithm:
near-real-time and reanalysed datasets, Atmos. Meas. Tech., 10, 4905–4914,
10.5194/amt-10-4905-2017, 2017.van den Broeke, M. R. and van Lipzig, N. P. M.: Changes in Antarctic
temperature, wind and precipitation in response to the Antarctic
Oscillation, Ann. Glaciol., 39, 119–126, 10.3189/172756404781814654,
2004.Voigt, C., Schreiner, J., Kohlmann, A., Zink, P., Mauersberger, K., Larsen,
N., Deshler, T., Kro, C., Rosen, J., Adriani, A., Cairo, F., Donfrancesco,
G. D., Viterbini, M., Ovarlez, J., Ovarlez, H., and David, C.: Nitric Acid
Trihydrate (NAT) in Polar Stratospheric Clouds, Science, 290, 1756–1758,
10.1126/science.290.5497.1756, 2000.von König, M., Bremer, H., Kleinböhl, A., Küllmann, H.,
Künzi, K. F., Goede, A. P. H., Browell, E. V., Grant, W. B., Burris,
J. F., McGee, T. J., and Twigg, L.: Using gas-phase nitric acid as an
indicator of PSC composition, J. Geophys. Res., 107, 8265,
10.1029/2001JD001041, 2002.Wang, D. Y., Höpfner, M., Blom, C. E., Ward, W. E., Fischer, H.,
Blumenstock, T., Hase, F., Keim, C., Liu, G. Y., Mikuteit, S., Oelhaf, H.,
Wetzel, G., Cortesi, U., Mencaraglia, F., Bianchini, G., Redaelli, G., Pirre,
M., Catoire, V., Huret, N., Vigouroux, C., De Mazière, M., Mahieu, E.,
Demoulin, P., Wood, S., Smale, D., Jones, N., Nakajima, H., Sugita, T.,
Urban, J., Murtagh, D., Boone, C. D., Bernath, P. F., Walker, K. A.,
Kuttippurath, J., Kleinböhl, A., Toon, G., and Piccolo, C.: Validation of
MIPAS HNO3 operational data, Atmos. Chem. Phys., 7, 4905–4934,
10.5194/acp-7-4905-2007, 2007.Wang, X. and Michelangeli, D. V.: A review of polar stratospheric cloud
formation, China Part., 4, 261–271, 10.1016/S1672-2515(07)60275-9,
2006.Wegner, T., Grooß, J.-U., von Hobe, M., Stroh, F., Suminska-Ebersoldt,
O., Volk, C. M., Hösen, E., Mitev, V., Shur, G., and Müller, R.:
Heterogeneous chlorine activation on stratospheric aerosols and clouds in the
Arctic polar vortex, Atmos. Chem. Phys., 12, 11095–11106,
10.5194/acp-12-11095-2012, 2012.Weiss, A. K., Staehelin, J., Appenzeller, C., and Harris, N. R. P.: Chemical
and dynamical contributions to ozone profile trends of the Payerne
(Switzerland) balloon soundings, J. Geophys. Res.-Atmos., 106, 22685–22694,
10.1029/2000JD000106, 2001.Wespes, C., Hurtmans, D., Clerbaux, C., Santee, M. L., Martin, R. V., and
Coheur, P. F.: Global distributions of nitric acid from IASI/MetOP
measurements, Atmos. Chem. Phys., 9, 7949–7962,
10.5194/acp-9-7949-2009, 2009.Wespes, C., Hurtmans, D., Emmons, L. K., Safieddine, S., Clerbaux, C.,
Edwards, D. P., and Coheur, P.-F.: Ozone variability in the troposphere and
the stratosphere from the first 6 years of IASI observations (2008–2013),
Atmos. Chem. Phys., 16, 5721–5743, 10.5194/acp-16-5721-2016,
2016.Wespes, C., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: O3 variability
in the troposphere as observed by IASI over 2008-2016: Contribution of
atmospheric chemistry and dynamics, J. Geophys. Res.-Atmos., 122,
2429–2451, 10.1002/2016JD025875, 2017.
WMO: Scientific Assessment of Ozone Depletion: 2014, Global Ozone Research
and Monitoring Project – Report No. 55, World Meteorological Organization,
Geneva, Switzerland, 2014.Wohltmann, I., Lehmann, R., Rex, M., Brunner, D., and Mäder, J. A.: A
process-oriented regression model for column ozone, J. Geophys. Res., 112,
D12304, 10.1029/2006JD007573, 2007. Wohltmann, I., Lehmann, R., and Rex, M.: Update of the Polar SWIFT model for
polar stratospheric ozone loss (Polar SWIFT version 2), Geosci. Model Dev.,
10, 2671–2689, 10.5194/gmd-10-2671-2017, 2017.
Wolter, K. and Timlin, M. S.: Monitoring ENSO in COADS with a seasonally
adjusted principal component index, in: Proceedings of the 17th Climate
Diagnostics Workshop, NOAA/NMC/CAC, CNSSL, IMMS and the School of Meteor.,
University of Oklahoma, Norman, OK, 52–57, 1993.Wolter, K. and Timlin, M. S.: Measuring the strength of ENSO events – how
does 1997/98 rank?, Weather, 53, 315–324,
10.1002/j.1477-8696.1998.tb06408.x, 1998.