We present the first observational dataset of vertically resolved global stratospheric BrONO2 distributions from July 2002 until April 2012 and compare them to results of the atmospheric chemical climate model
ECHAM/MESSy Atmospheric Chemistry (EMAC). The retrieved distributions are based on space-borne measurements of infrared limb-emission spectra recorded by the Michelson Interferometer for
Passive Atmospheric Sounding (MIPAS) on Envisat. The derived vertical
profiles of BrONO2 volume mixing ratios represent 10∘ latitude
bins and 3 d means, separated into sunlit observations and observations in the dark. The estimated uncertainties are around 1–4 pptv, caused by spectral noise for single profiles as well as for further parameter and
systematic errors which may not improve by averaging. Vertical resolutions
range from 3 to 8 km between 15 and 35 km altitude.
All leading modes of spatial and temporal variability of stratospheric
BrONO2 in the observations are well replicated by the model
simulations: the large diurnal variability, the low values during polar winter
as well as the maximum values at mid and high latitudes during summer. Three major differences between observations and model results are observed: (1) a
model underestimation of enhanced BrONO2 in the polar winter
stratosphere above about 30 km of up to 15 pptv, (2) up to
8 pptv higher modelled values than observed globally in the lower
stratosphere up to 25 km, most obvious during night, and (3) up to 5 pptv lower modelled concentrations at tropical latitudes between 27
and 32 km during sunlit conditions. (1) is explained by the model
missing enhanced NOx produced in the mesosphere and lower
thermosphere subsiding at high latitudes in winter. This is the first time
that observational evidence for enhancement of BrONO2 caused by
mesospheric NOx production is reported. The other major
inconsistencies (2, 3) between EMAC model results and observations are studied by sensitivity runs with a 1D model. These tentatively hint at a model underestimation of heterogeneous loss of BrONO2 in the lower stratosphere, a simulated
production of BrONO2 that is too low during the day as well as strongly underestimated BrONO2 volume mixing ratios when loss via reaction with
O(3P) is considered in addition to photolysis. However, considering
the uncertainty ranges of model parameters and of measurements, an unambiguous
identification of the causes of the differences remains difficult.
The observations have also been used to derive the total stratospheric bromine
content relative to years of stratospheric entry between 1997 and 2007. With
an average value of 21.2±1.4pptv of Bry at
mid latitudes where the modelled adjustment from BrONO2 to Bry is smallest, the MIPAS data agree with estimates of
Bry derived from observations of BrO as well as from
MIPAS-Balloon measurements of BrONO2.
Introduction
Besides chlorine, bromine is the major halogen constituent, with anthropogenic and natural sources affecting stratospheric ozone
e.g.. After had described the possible relevance of bromine for ozone, the important role of
bromine nitrate (BrONO2) within stratospheric bromine chemistry was
proposed by . They noticed the much faster photolysis of
BrONO2 compared to ClONO2, which is an important prerequisite
for the effectiveness of bromine ozone destruction cycles compared to those of
chlorine .
BrONO2 is produced via the termolecular reaction and references
thereinBrO+NO2⟶MBrONO2.
Due to its relatively short lifetime, the BrONO2 concentration is
strongly coupled to changes in NO2. The 1σ uncertainty factor of the reaction rate (Reaction ) as provided
by is 1.2 (i.e. 20 %
uncertainty) at 298 K, increasing to ∼1.9 at a stratospheric temperature of 220 K.
The main loss process of BrONO2 during the day is photolysis and references therein:
BrONO2+hν→Products,
in which the products are Br+NO3 and BrO+NO2. The recommended quantum yields at wavelengths above
300 nm, being most important in the lower stratosphere, are 0.85 and
0.15, respectively. While in a combined uncertainty in
cross sections and quantum yields of 1.4 is provided, the most recent
evaluations assign one
wavelength-independent uncertainty factor of 1.2 (2σ) to the cross
sections.
Further loss of BrONO2 is due to atomic oxygen :
BrONO2+O(3P)→BrO+NO3,
which occurs, like Reaction (), only during sunlit conditions due to the necessary presence of O(3P). The 1σ uncertainty factor for the reaction coefficient varies between 1.25 at room temperature and 1.3 at
220 K. However, independent confirmation of
the reaction parameters of Reaction () is pending
.
Finally, heterogeneous reactions can affect BrONO2 concentrations,
like hydrolysis in sulfuric acid aerosols and references
therein,
BrONO2+H2O(s,l,H2SO4⋅nH2O)→HOBr+HNO3,
or in combination with halogens at surfaces, like
BrONO2+HCl(H2O(s),H2SO4⋅nH2O)→BrCl+HNO3,
where typical uncertainty factors of the gas–surface reaction probabilities are in the range of 2–4 .
Given the relatively large uncertainties in most of these leading reactions
involving BrONO2, comparison of observations to model calculations can
be helpful for verification or even for suggesting improvements. For example,
analysed stratospheric balloon observations and concluded
that the ratio JBrONO2/kBrO+NO2 should be
increased to fit their data. Such investigations can be useful, first, to
improve model simulations of stratospheric ozone loss and, second, to aid the
analysis of the total stratospheric bromine (Bry) content from
observations of one species, such as BrOe.g..
Anthropogenic and natural emissions both contribute roughly equally to the
present-day stratospheric bromine loading: give a best
estimate of the total stratospheric bromine loading for 2016 of
19.6 pptv, of which natural sources contribute slightly more than
10 pptv. Brominated very short-lived substances (VSLSs), such as
bromoform (CHBr3) and dibromomethane (CH2Br2), contribute about 5 pptv to the stratospheric bromine loading, but their precise
current contribution, any possible long-term changes, and the additional influx of inorganic product gases (product gas injection, PGI) are still
uncertain e.g.. In this context, the observation of BrONO2 provides an additional independent
approach to determine total Bry and, in
consequence, to estimate the relative contribution of brominated VSLSs.
Due to its spectral lines in the microwave and UV-vis, remote-sensing observations of BrO, the major inorganic bromine species in the lower
stratosphere during sunlit hours, are common from the ground e.g., from aircraft e.g., from
balloons
e.g., and from satellites
e.g.. In
contrast, BrONO2, the most important night-time reservoir of bromine,
was detected in infrared limb-emission observations by the Michelson
Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board
the Envisat satellite only a decade ago . At that time the
retrieval of altitude profiles was complicated by uncertainties in the
infrared absorption cross sections of BrONO2. In the meantime, provided an improved infrared spectroscopic database
covering stratospheric conditions. On the basis of these new data, analysed the diurnal variation of BrONO2 during
three flights of the MIPAS-Balloon instrument.
In this paper we introduce the first day- and night-time climatology of
stratospheric BrONO2 as derived for the ∼10-year lifetime of MIPAS/Envisat. We compare the results to global model simulations and discuss
major differences by use of 1D photochemical modelling. Finally, the total
stratospheric Bry content is estimated.
MethodsMIPAS instrument and data analysis
Flying on the polar-orbiting satellite Envisat, the limb sounder MIPAS recorded infrared spectra of the atmospheric thermal emission from 2002 until 2012 . MIPAS was operated in two major modes: during
period 1 (P1), between July 2002 and March 2004, the spectral resolution,
defined here as 0.5× (maximum optical path difference)-1, was
0.025 cm-1, and during period 2 (P2) between January 2005 and April
2012, the resolution was set to 0.0625 cm-1. During P1 the spectra
of the “nominal” viewing modes as used in this work were taken at 17 tangent
points between 7 and 72 km with 3 km steps up to
42 km and somewhat larger steps above. During P2, 27 spectra were recorded per limb scan with latitude-dependent tangent altitudes ranging from 5–70 km at the poles to 12–77 km over the Equator, with
steps increasing with height from 1.5 to 4.5 km. The along-track
sampling distance between each limb scan was ∼550km during P1 and ∼420km during P2. The local solar Equator-crossing time at the position of the tangent points is around 10:10 for the descending node and
22:20 for the ascending node of the Sun-synchronous orbit.
Since the first stratospheric detection of BrONO2, retrievals from averaged MIPAS spectra have been
established for species with very weak signatures, such as SO2 and
NH3. The retrieval of vertical
profiles of BrONO2 volume mixing ratios as applied for the current
dataset follows closely the procedure described in . Here
we briefly describe the retrieval scheme as well as the applied improvements
with respect to .
For the selection of spectra to be averaged zonally as well as temporally, the
cloud filter method by has been applied to sort out any
measurements affected by tropospheric as well as polar stratospheric
clouds. Further, only spectra above about 15 km tangent altitude have
been used for averaging so as to concentrate mainly on the stratosphere. We have applied a constrained nonlinear multiparameter least-squares fitting
procedure to each limb sequence of averaged spectra to derive profiles of trace gas volume mixing ratios at 1 km-spaced vertical levels. Here we have used the same spectral interval (801–820 cm-1) and
atmospheric parameters simultaneously fitted with BrONO2 (O3,
ClONO2, NO2, CFC-22, HNO4, COF2,
HNO3, ClO, CCl4, CFC-113, PAN, T) as in
. We have applied a first-order smoothing constraint
to dampen oscillations in the retrieved
profiles. The regularization strength for each of the simultaneously derived
species has been adjusted separately, and the related a priori profile for the target species BrONO2 was set to 0.1 pptv, while for the other species climatological profiles have been used.
Major improvements and updates compared to are the following.
The most recent version (V8.03) of level-1B calibrated limb radiances by the European Space Agency (ESA) has been used
(https://earth.esa.int/web/sppa/mission-performance/esa-missions/envisat/mipas/products-availability/level-1/level1-8.03, last access: 14 December 2021).
To simulate the spectral feature of BrONO2, the new
pressure- and temperature-dependent infrared spectroscopic database by
has been used.
The spectroscopy of the interfering gases has been taken from the
high-resolution transmission molecular absorption database (HITRAN) 2016
with the exception of HO2NO2. For this gas, the
infrared cross sections in HITRAN for 220 K by have been extended by the ones of , which were measured at
298 K, to account for different atmospheric temperatures by two-point
interpolation .
While in retrievals have been performed based
on monthly mean spectra of September 2002 and 2003 within few coarse
latitude bands, here we have subdivided the MIPAS measurements into 18
latitude bands of 10∘ spacing with a temporal binning of 3 d over the whole observational period 2002–2012.
We have estimated altitude-dependent errors of the BrONO2 retrieval by applying assumptions about single error sources to two randomly selected
periods in March and June for the years 2003 and 2009, i.e. during P1 and P2, respectively. The results are shown in Fig. together
with the total error profile calculated by quadratic combination of single
error components. Instrumental uncertainties are estimated at 3 % for
the instrument line shape expressed as linear loss of modulation efficiency
toward the maximum optical path difference of the interferometer (ILS),
1 % for radiometric gain calibration (RadGain), and 300 m for
tangent height knowledge (Htang). The uncertainty of European Centre for
Medium-Range Weather Forecasts (ECMWF) temperatures (Temp) has been set to
values of 2 K below and 5 K above 35 km altitude. The
uncertainty of the BrONO2 spectroscopy has been assumed to be 5 %, which is on the conservative side considering the 2 %
(1σ) error estimation given in . Further errors
refer to the spectroscopic parameters of interfering gases. For those, we have
assumed uncertainties of 5 % for species described by cross sections (SpecXitf), 5 % for intensities (SpecINTitf) and 10 % for
the half-widths of the line parameters (SpecHWift). These assumptions are within the typical errors provided in the database . A further error term describing the retrieval from averaged spectra (NonLin) has
been estimated on the basis of dedicated retrieval simulations as detailed in
. For this estimate the values used for the tangent
altitude scatter of single observations were set to 400 and 300 m
(1σ) during P1 and P2, respectively.
Retrieval error estimates
from four 3 d periods, two during P1 (a, b) and two during P2
(c, d), for both dark (a, c) and sunlit
(b, d) conditions. Considered error sources are the uncertainties of the instrumental line shape and radiometric gain calibration (ILS, RadGain), the pointing knowledge (Htang), assumed temperature profiles (Temp), spectroscopic errors of BrONO2 absorption cross sections (SpecBrONO2) and errors in cross sections (SpecXitf), line half-widths (SpecHWift) and line intensities (SpecINTitf) of interfering species, as well as the error due to the applied technique of retrievals from averaged spectra (NonLin) and the spectral noise of the instrument (Noise).
The total error (Total err) has been determined by quadratic combination of all single error components, while the combined parameter and systematic error (Tot paraerr) considers all uncertainties except the spectral noise. The blue-filled space around the total error curves indicates the areas into which 90 % of the total error profile estimates fall.
The total error estimate as calculated by quadratic combination of the single components is given by the blue lines in Fig. . Around this
total error estimate, the blue shading indicates the variability of the
estimated errors for all latitude bands. In general, the estimated total
errors vary between about 1 and 4 pptv, independent of day- or
night-time observations. They appear to be slightly smaller during P1 compared
to P2, which is probably due to the better spectral resolution during P1.
Further, it is evident from Fig. that, below 20–23 km, total parameter errors and spectral noise are the dominant
contribution to the total error, while at larger altitudes it is mostly
dominated by spectral noise.
Examples of the vertical resolution of the MIPAS BrONO2 retrieval as derived from the diagonal elements of the averaging kernel matrices. Given curves are averages over all latitude bands for the given periods during P1 and P2. The zigzag during P1 is caused by the constant tangent altitude grid, while during P2, the variation of tangent altitudes with latitude smears out this effect. Note that the vertical resolution is generally finest at the tangent points and coarsest in between.
As a further diagnostic measure of the retrieval, Fig. shows
the vertical resolution as a function of altitude. It has been calculated by
dividing the retrieval grid width of 1 km by the diagonal elements of
the averaging kernel matrices . The vertical resolution
is about 3 km at 15 km altitude and becomes coarser with
altitude, reaching 8 km at 35 km altitude. The vertical
resolution is generally finer at the tangent altitudes and coarser at
retrieval levels between the tangent altitudes. Conversely, the retrieval
noise is larger at the tangent altitudes and smaller at altitude levels in
between. These effects are only visible when a retrieval set-up is chosen where the retrieval grid is finer than the tangent altitude spacing. Since in period
P1 the tangent altitude grid is fixed, this effect survives the averaging,
leading to a zigzag profile of the vertical resolution. In contrast, in P2 the
tangent altitude grid varies with latitude, and the zigzag features of
vertical resolution average out.
Atmospheric modelling
We have compared the MIPAS BrONO2 dataset with a multi-annual
simulation from the chemical climate model ECHAM/MESSy Atmospheric Chemistry
(EMAC) . Within EMAC, the interface Modular Earth Submodel
System (MESSy) links the sub-models describing tropospheric and middle
atmospheric processes to the dynamical core, the fifth-generation European
Centre Hamburg general circulation model ECHAM5 . We
have used EMAC (ECHAM5 version 5.3.02, MESSy version 2.52) at the T42L90MA resolution with 90 vertical hybrid pressure levels from the ground up to 0.01 hPa (∼80km) and a horizontal resolution of
∼ 2.8∘×2.8∘ latitude × longitude. The sub-models MECCA and MSBM
simulate gas-phase chemistry and polar stratospheric
clouds including heterogeneous reaction rates, respectively. To reproduce
realistic conditions for comparison with the observations, the model run was
nudged towards the ECMWF reanalysis ERA-Interim by a
Newtonian relaxation technique of surface pressure, temperature, vorticity,
and divergence above the boundary layer and below 1 hPa. We have applied a comprehensive chemistry set-up from
the troposphere to the lower mesosphere with more than 100 species involved in
gas-phase, photolysis, and heterogeneous reactions on liquid sulfate aerosols,
nitric acid trihydrate (NAT), and ice particles. Rate constants for gas-phase reactions have been taken mainly from and the Jet
Propulsion Laboratory (JPL) compilation . Photochemical
reactions of short-lived bromine-containing organic compounds CHBr3, CH2Br2, CH2ClBr, CHClBr2, and CHCl2Br are
included in the model set-up . Boundary conditions for
CH3Br and the bromine-containing halons were taken from
and extended with the RCP6.0 scenario as suggested by
. We have used scenario 5 of to
describe the surface emissions of these organic bromine species. During the
MIPAS measurement periods, from the model output first all data within 1 h around 10:00 and 22:00 LT were selected. Depending on
their latitude, longitude and altitude, they were then assigned to sunlit and
dark conditions and averaged over the observational bins of 10∘
latitude and 3 d periods. In the extreme cases of twilight conditions at high latitudes, this might induce differences between modelling and observations. However, in the discussion below we only refer to situations
which are not affected by twilight conditions.
For specific sensitivity investigations at low latitudes, we have applied a 1D photochemical stacked box model. The chemical mechanism of the 1D model is
based on the SLIMCAT model and references
therein. The 1D model runs have been initialized with
equatorial mean profiles of the EMAC simulation of all inorganic Bry
species and NO2. For the other species as well as pressure and
temperature, equatorial profiles from the MIPAS reference database have been
used . A comparison of the parameters of several bromine
reactions between EMAC, the 1D baseline model run and the JPL2019 compilation
is provided in Table .
The MIPAS dataset in comparison to EMAC model resultsOverview of the measurements
We provide overviews of the MIPAS BrONO2 volume mixing ratio datasets
at full temporal resolution in the left-hand-side panels of Figs. and for observations during dark (night) and sunlit (day)
conditions, respectively. White spaces indicate regions where no measurements
are available, such as observations in the dark during high-latitude summer as
well as sunlit measurements during winter. A measurement gap due to
instrumental issues of MIPAS happened between April 2004 and January 2005, and in the subsequent years, observations were ramped up through to about 2007. From
then on, quasi continuous coverage exists until April 2012. The coverage at
lower altitudes is determined by the lower limit of 15 km, chosen to
confine the retrievals primarily to the stratosphere, and by the presence of
high-altitude clouds and the scan pattern of MIPAS (which is mainly a factor
in the tropics). Some additional data gaps exist at high southern latitudes
during winter, when thick polar stratospheric clouds (PSCs) obscured the observations.
Horizontal cross sections (latitude versus time) of
measured (left) and modelled (right) BrONO2 volume mixing ratios at selected altitudes over the whole time period of MIPAS observations during dark conditions. Retrieval averaging kernels have been applied to model data. White areas indicate the absence of measurements.
Same as in Fig. but for sunlit measurements.
From Figs. and , the major features of the stratospheric BrONO2 variability can be discerned in our
measurements.
The diurnal variability (Fig.
versus Fig. ) as a manifestation of the fast photolysis during the day (Reaction ) versus the production (Reaction ).
The annual recurrence of low values during night at high latitudes
(Fig. ) due to the lack of NOx as a supply
for the production (Reaction ) in combination with
heterogeneous loss due to the presence of PSC particles
(Reactions and ).
The annual maxima of BrONO2 volume mixing ratios at high and mid latitudes during day- and night-time observations in summer caused
by the annual variability of NO2.
The lack of a similarly clear seasonal variability at tropical latitudes.
We have reduced the dataset to annual views by averaging over the whole MIPAS
observational period in order to provide a clearer picture of intra-annual variabilities. The related horizontal and vertical cross sections are presented in Figs. –. During
dark conditions, maximum mean mixing ratios of around 22 pptv are
reached mainly at mid latitudes at altitudes of 25–30 km during the summer months, while in winter only about 18 pptv is observed. During sunlit conditions, the largest BrONO2 mixing ratios of up to about
11 pptv appear at high latitudes at 20–25 km altitude during summer. Negative retrieved mean mixing ratios in the sub-tropics and tropics
below about 18–22 km altitude are discussed more in detail below.
Horizontal cross sections (latitude versus time) of the annual development of measured (left) and modelled (right, with averaging kernels applied) BrONO2 volume mixing ratios during dark conditions at selected altitudes calculated as average values over the whole MIPAS dataset in weekly bins (see Fig. ).
Same as Fig. but for sunlit measurements (see Fig. ).
Bimonthly averaged cross sections (altitude versus latitude) of BrONO2 volume mixing ratios for dark conditions. Left: measurements, middle: model with retrieval averaging kernels applied, right: model minus MIPAS.
Same as Fig. but for sunlit conditions.
Measurement–model comparisons
The results of the EMAC model run are presented in the second column in each
of Figs. –. To take into account the limited vertical resolution of the measurements for these
comparisons, we have applied the averaging kernel matrix of each retrieved
profile of BrONO2 (see Sect. ) to the related
modelled profile. Differences between model results with and without averaging
kernel application are generally below 1 pptv, with only sporadic exceptions at the highest altitudes near polar latitudes, where differences reach
up to 3–4 pptv (Figs. and
).
From comparing measured and modelled distributions of BrONO2 volume
mixing ratios in Figs. –, it is evident that the model reproduces all major modes of variability which are
present in the observations as described in the previous section. Despite
this agreement, there are a few areas where systematic deviations are
prominent.
The most obvious differences appear (1) at higher altitudes during polar
winter, (2) in the lower stratosphere mainly at mid and low latitudes during the entire year as well as (3) at altitudes around 30 km in the
tropics during sunlit conditions. We discuss these differences one by one
below.
One disagreement between modelled BrONO2 and MIPAS observations can
best be observed in Figs. and
: at altitudes of around 30 km and above for
latitudes south of 70∘ S, the model predicts values smaller than
5 pptv from May until September. The corresponding measurements,
however, reach values of 10 to 15 pptv. While not as pronounced, this
feature is also visible in the Arctic wintertime stratosphere, with model estimates of 5–10 pptv and measurements of 10–15 pptv. We
explain these low model values of BrONO2 by an underestimation of
NO2 as visible in Fig. . In this figure, the MIPAS NO2 distributions are compared to the EMAC model results of
NO2. The missing NO2 in the simulations is due to an
insufficient supply of NOx through downward propagation from the
upper mesosphere and lower thermosphere. This stratospheric enhancement of
NOx through production by energetic particle precipitation and
downwelling during polar winter has been investigated e.g. on the basis of MIPAS observations by .
High modelled night-time <inline-formula><mml:math id="M208" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">BrONO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> at lower altitudes
As visible e.g. in Figs. and
, the model overestimates BrONO2 volume
mixing ratios with respect to the MIPAS results by up to 8 pptv in the
lower stratosphere at altitudes up to about 25 km during the night. Such differences are also present during the day, albeit to a smaller absolute extent
(up to about 4 pptv; see Fig. ). Relative differences between measurements and model results during the day and night are
more similar, reaching e.g. 50 % at around 20 km at mid latitudes and 22–25 km in the tropics. Below, the day-time relative differences become much larger due to the very small mixing ratios of
BrONO2 and are difficult to compare.
Sensitivity 1D model simulations for testing of the night-time EMAC model overestimation of BrONO2 below 27 km altitude for BrONO2 (top row), BrO (middle row), and HOBr (bottom row). Left: diurnal evolution of the “1D base” run for a period of 2 d. The white dashed lines indicate the local solar times of the MIPAS observations. The columns “night” and “day” contain the averaged night-time and day-time profiles of MIPAS at 5∘ S as black solid lines along with error bars indicating the 2σ estimated measurement uncertainty. The mean EMAC results are provided in red. The other curves illustrate the results of the 1D model simulations and the columns “diff night” and “diff day” show the differences between the 1D sensitivity runs and the “1D base” run.
The black line in panel “night” of the top row in Fig.
represents the measured mean night-time profile over the whole period at
5∘ S. The related EMAC model result is shown in red. Maximum
differences between both are about 8 pptv at around 20–22 km
altitude. As possible explanations for these differences we will discuss below (1) measurement errors, (2) wrong modelling of the release of Bry
from its source gases, (3) wrong partitioning of Bry between its
main constituents in the model simulations, and (4) wrong modelling of NO2 mixing ratios.
The degree of discrepancy between measurement and model of up to
8 pptv over an altitude range of around 5 km cannot reasonably
be explained given the errors estimated for the MIPAS BrONO2 retrieval
(Fig. ). This would allow for discrepancies of around
2–3 pptv, especially considering that the “noise” error term is
strongly reduced by the temporal averaging over all equatorial
measurements. Still, one cannot rule out unequivocally any unidentified additional systematic error source in the measurements contributing to these
differences.
report on balloon observations of BrO in the
framework of an ENVISAT validation campaign in Teresina, Brazil (5.1∘ S, 42.9∘ W), on 17 June 2005. In the altitude
region of 20–22 km they observed volume mixing ratios of BrO
of around 6–10 pptv, which agrees with our EMAC model results during day time (see Fig. , second row, red line in panel “day”) that indicate 8–11 pptv of BrO in the same altitude range. Similar mixing ratios of BrO at these altitudes in the tropics
have also been reproduced by other model simulations
e.g. as well as by satellite observations . A possible
contribution to a model overestimation of BrONO2 might be the emission
scenario used for organic bromine species, taken from .
As has e.g. been shown by , this scenario probably leads to
an overestimation of brominated VSLS by up to 2 pptv, which is,
however, not enough to explain the observed differences. Thus, it is highly
unlikely that the inorganic bromine content at 20–25 km is strongly
overestimated in the EMAC model calculations.
The modelled partitioning of Bry at those altitudes during
night is essentially determined by the heterogeneous conversion of
BrONO2 into HOBr through sulfate aerosols
(Reaction ). For a more detailed investigation, we have performed sensitivity simulation with the 1D model, the results of which are shown in
Fig. . The rate coefficient for heterogeneous reactions
is proportional to the aerosol surface area density (SAD) and the reaction
probability γ. For the baseline 1D calculation, we have applied a
vertical profile of aerosol surface area densities which was derived from the
mean tropical aerosol volume densities as available from MIPAS
assuming a lognormal size distribution with a number
density of 10 cm-3 and a width of
1.8. Figure provides a comparison of the resulting
“MIPAS mean” surface area densities with SAGE II profiles in the tropics
between July 2002 and August 2005 as well as in situ measurements at mid latitudes over Laramie (41∘ S,
105∘ W) between 2002 and 2012 . The
reaction probabilities for the 1D baseline run (dashed pink curve in Fig. b) were determined according to as reported by .
A test without consideration of heterogeneous conversion of BrONO2
into HOBr (dotted orange line in Fig. ) results in
a night-time increase in BrONO2 by up to 5 pptv as well as the corresponding decrease in HOBr. To replicate the observations of BrONO2, we performed two tests by adjusting the profile of aerosol
surface area density: one (a) where we kept the altitude-dependent reaction
probabilities (dashed pink curves in Figs. and
) and one (b) where the reaction probabilities were set
to 1 over the entire altitude range (dashed-dotted purple curves in Figs. and ). As can be seen from
Fig. , in test (a) one would require surface area
densities enhanced by factors of more than 3 to deplete BrONO2 to such
an extent that the observations in the altitude range 20–26 km are
met. For the idealized case of γ=1, the aerosol surface area densities
had to be adjusted up to 24 km only, however, still by a factor of
more than 3 at 20 km altitude.
As the formation of BrONO2 is strongly linked to NO2, we have inspected its modelled mixing ratios in comparison to the observed
ones. As can be seen in Figs. and
, the model calculations agree well with the
measurements, especially at the lower altitudes where the observed discrepancies in BrONO2 occur. Thus, we exclude a wrong modelling of
NO2 as an explanation for these differences.
In summary, we have to conclude that there is no compelling evidence for any
of the explanations above causing the observed differences between
measurements and model results. While (3) would imply an increase in aerosol surface area density contradicting related observations, (2) would oppose
observations of BrO, (4) provides no hint of a discrepancy, and (1) seems out of reach within our estimated retrieval errors for BrONO2
from MIPAS.
(a) Vertical profiles of surface area densities (SADs). Blue: mean MIPAS tropical profile (see text for details), orange: mean profile from in situ observations over Laramie (41∘ S, 105∘ W) between 2002 and 2012, green: mean SAGE II profiles in the tropics between 2002 and 2005 . The dashed pink and dash-dotted purple lines show the adapted SAD profiles corresponding to the 1D model results indicated by the same line styles in Fig. . (b) Reaction probability profiles for hydrolysis of BrONO2 (Reaction ). The dashed pink profile refers to the standard one determined in the 1D model , and the dash-dotted purple curve has been used in the sensitivity analysis to adjust the SAD (a and Fig. ).
The EMAC model calculations fit the observed night-time BrONO2 maximum
at 29–30 km altitude in the tropics quite well (see
Fig. ). However, during day time (Fig. ), BrONO2 is almost entirely depleted in
the EMAC simulations at these altitudes, while MIPAS still detects maximum values of up to 5 pptv. Possible reasons for this discrepancy
discussed below might be (1) measurement errors or (2) the impact of uncertainties in the parameters of Reactions () and
(). Further, we will address the effect of Reaction (), which is not considered in the present implementation
of EMAC. Finally, under (3), we will briefly address differences between
measured and modelled NO2 mixing ratios.
In the top row of Fig. , the mean profile of BrONO2 measured at 5∘ S is again shown together with the
EMAC model results in panel “day”. As in the case of the night-time
discrepancy, the values of BrONO2 observed during sunlit conditions
cannot be explained by the estimated retrieval errors, which are about
1–2 pptv at the 30 km region
(Fig. ). However, as can be seen in
Fig. , the mean measured profile values below about
24 km are negative, indicating an unresolved issue in the retrieval at
these altitudes. Still, the negative values are in the range of the estimated uncertainties. To investigate whether the positive values above where the discrepancy with the model becomes apparent might be due to an oscillatory
feature caused by the negative values below, we have tested different
retrieval options (increasing the regularization strength, performing
retrievals of log(vmr) instead of vmr, skipping tangent altitudes below). In
all the tests, the maximum values at around 30 km still appeared in the retrieved profiles, indicating them as a robust feature.
Sensitivity calculations using the 1D model are also shown in
Fig. . As a baseline for these simulations, we have
applied the setting “SAD adapted” so that the calculations during night-time also fit to the observations at altitudes below 27 km. The resulting
profile from the 1D model run also shows values which are smaller than the
observed BrONO2 mixing ratios by about 3 pptv at around
30 km altitude. Though about 1 pptv larger than the EMAC simulations at those altitudes, the 1D results are also not compatible with
the measurements.
To test the sensitivity of modelled BrONO2 to its production via the three-body Reaction (), we have used the JPL2019 formulation
instead of that by that was
applied in the EMAC and 1D baseline runs (Table ). This led to
an increase in BrONO2 vmr values by about 1 pptv; see the dash-dotted green curve in Fig. . Further increasing
these rate coefficients by a factor of 2, which is well covered by the 2σ uncertainty factor of 3.7 for that reaction at 220 K, led to an additional increase by about 3 pptv (dash-dotted olive curves in Fig. ). While
these results coincide now with the observed day-time abundances of BrONO2 at around 30 km, the increase at lower altitudes does
not correspond well to the observations there. Moreover, around 30 km during night, the calculations overestimate the measurements by up to 2 pptv.
To test the sensitivity with respect to the photolysis of BrONO2
(Reaction ), we have divided the photolysis rate by 1.2, the
2σ uncertainty as provided by . The result is
illustrated by the blue dashed curves in Fig. . The
resulting increase of only around 0.5 pptv is too small compared to
the observations. Moreover, the increase appears over a larger altitude range
(from 22 up to 34 km) compared to the more confined region between
about 27 and 33 km where the increased day-time values are observed.
The grey dotted curve in Fig. illustrates the effect on
the simulated mixing ratios when the loss Reaction () is included
in the 1D model by using the reaction coefficients from the JPL2019
compilation; see Table . With a reduction of up to 1.5 pptv, this obviously drives the concentrations
further away from the observations.
In the tropics at around 30 km, the mixing ratios of NO2
from the measurements are around 10 %–20 % smaller than
modelled by EMAC, with the results of the 1D model lying in between (see Figs. and ). These
deviations cannot account for the model underestimation of BrONO2
since they would even imply stronger modelled production of BrONO2.
In conclusion, we have found no unequivocal explanation for the high measured
day-time mixing ratios of BrONO2 at around 30 km over the tropics/subtropics: (a) the differences between models and observations are
outside the estimated measurement errors, (b) the uncertainties of the cross
sections for photolysis (Reaction ) of BrONO2 are by
far too small, (c) the error estimates for Reaction () would
allow a sufficient increase in BrONO2 mixing ratios but over an overly large vertical extent, and (d) any inclusion of Reaction () opens the gap between simulated and observed BrONO2 concentrations even
more.
Sensitivity 1D model simulations to test the day-time EMAC model underestimation of BrONO2 around 29 km altitude for BrONO2 (top row), BrO (middle row), and HOBr (bottom row). Left: diurnal evolution of the “1D base” run for a period of 2 d. The white dashed lines indicate the local solar times of the MIPAS observations. The columns “night” and “day” contain the averaged night-time and day-time profiles of MIPAS at 5∘ S as black solid lines along with error bars indicating the 2σ estimated measurement uncertainty. The mean EMAC results are provided in red. The MIPAS and EMAC curves are the same as in Fig. . The other curves illustrate the results of the 1D model simulations and the columns “diff night” and “diff day” show the differences between the 1D sensitivity runs and the “1D base” run.
Estimation of total stratospheric <inline-formula><mml:math id="M328" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">Br</mml:mi></mml:mrow></mml:math></inline-formula><inline-formula><mml:math id="M329" display="inline"><mml:msub><mml:mi/><mml:mi>y</mml:mi></mml:msub></mml:math></inline-formula>
The MIPAS dataset of BrONO2 allows us to determine the total
stratospheric content of bromine (Bry) by using the so-called
inorganic method. It is a linear correction of the observed bromine species by
multiplication by the ratio between total modelled bromine (Brymod) and the modelled bromine species observed. This
procedure has often been applied in case of day-time observations of BrOe.g.. were the first to apply this method to night-time observation of BrONO2:
Bry=BrONO2meas×BrymodBrONO2mod.
Using night-time observations of BrONO2 instead of BrO measured
during the day to derive Bry should have the advantage that BrONO2 during the night makes up a larger fraction of Bry than
BrO does during the day, due to the continuous production of BrONO2 (Reaction ).
The modelled night-time ratio between BrONO2mod and
Brymod has been examined to decide which region (in terms
of altitude and latitude) to use for determination of Bry from the
MIPAS dataset (cf. Fig. ). Values clearly exceeding
90 % are simulated at mid latitudes, mainly during spring/summer/autumn, and centred at altitudes around 26 km. Thus, for the analysis we have chosen data at 25–26 km altitude and
40–60∘ latitude from October to March in the Southern Hemisphere and from April to September in the Northern Hemisphere. Additionally, due to the low seasonal variability of BrONO2 and to capture relatively young air
masses, we also considered tropical data at 29–30 km in the analysis. While not exceeding 90 %, the ratio
BrONO2mod--Brymod is still larger than 85 % (see Fig. ).
For stratospheric measurements, the values of derived total inorganic bromine
are generally assigned to their year of entry into the stratosphere
e.g.Fig. 1-16. Here we have used an update of the age-of-air dataset determined from MIPAS retrievals of SF6. Typical values of age of air in the case of the mid-latitude observations range between 5 and 6.5 years and in the tropics
between 3.5 and 5 years, with errors of about 1 year.
Depending on the date of stratospheric entry, total Bry (red)
estimated from the MIPAS observations through Eq. () in
comparison to the original MIPAS observation (BrONO2meas, blue) is
shown in Fig. . The solid lines represent weighted means of
all data points, where for the weighting the a posteriori estimated random error (dotted black curves in Fig. ) was applied. The
resulting random error as indicated by the thickness of the line is very small
(0.04–0.07 pptv) due to the large number of data points. The major part of the uncertainty in our estimation of total Bry, however, is
due to the combined parameter and systematic error (the green line in
Fig. ), which is shown by the red shading in Fig. . It should be noted that this error term is a
combination of a variety of estimated uncertainties, each of which might also partly be random in nature with different temporal correlation lengths
(e.g. correlated over each of the two measurement phases). Thus, we have made
a conservative assumption in considering all those to be systematic – but possibly also underestimate them by applying quadratic combination of the single error terms.
Series of averaged MIPAS BrONO2 measurements (dark blue dots) and derived total stratospheric Bry (red dots) for different altitude and latitude bands over the time of stratospheric entry. Dark blue and red lines indicate the related time-averaged mean values over the whole period and the red shading indicates the estimated 1σ uncertainty. The other data points and error bars are estimates of Bry from observations of BrO taken from Fig. 1-16 in as well as updates of the MIPAS-Balloon observations of BrONO2.
Coarse locations (south of 20∘ S: top; 20∘ S–20∘ N: middle; north of 20∘ N: bottom) of the non-MIPAS/Envisat observations are indicated by the larger symbol size.
See also Table .
Our estimates of total Bry vary from 21.0±1.4 and
21.4±1.4pptv for the northern and southern mid-latitude regions,
respectively (years of stratospheric entry: 1997–2006), to a maximum of
22.4±1.7pptv in the tropical stratosphere (years of stratospheric
entry: 1998–2007) (Table ). The values of Bry from
the mid latitudes of both hemispheres coincide clearly within their uncertainty ranges. Since it is unlikely that the real values of total
Bry vary strongly in the stratosphere, the differences of 1–1.4 pptv between tropical and mid-latitudinal estimates are more
probably caused by uncertainties. These may either be caused by errors in the
BrONO2 concentrations derived from MIPAS or may be due to the calculation of total Bry from BrONO2 through Eq. ()
(or a combination of both). The first explanation would require a retrieval
error component varying with latitude, e.g. due to some temperature dependence, while the second one implies model uncertainty. Since the model adjustment of
Bry from BrONO2 is much larger in the tropical stratosphere
(about 2.5 pptv) than at mid latitudes (about 0.5 pptv), the second explanation would affect more strongly our estimation of Bry
in the tropics.
Mean values, standard deviation, the standard error of the mean, and the estimated accuracy of total stratospheric Bry as derived from MIPAS, from Fig. 1-16 of as well as single observations by the MIPAS-Balloon experiment during the stratospheric entry years 1997–2007; see also Fig. .
Bry obtained from MIPAS can be compared to data of Bry
derived from observations of BrO, as summarized for example in
Fig. 1-16 of . To provide an easy way of comparison, we have replicated the single values of those datasets in each panel of our
Fig. and have collected their respective mean values in
Table , limited to the period of stratospheric entry from the
MIPAS dataset. Notably, all of these observations are compatible with the
values derived from MIPAS and lie clearly within the uncertainty estimates of
MIPAS data. The balloon-borne observations ranging from about 20.4 to
21.3 pptv are more in line with the mid-latitude values of MIPAS, as are the ground-based observations from Harestua with 21.0 pptv. The
Bry value of 22.4 pptv from the ground-based observations in
Lauder fits, however, more to the higher tropical MIPAS estimates. Further,
the Bry estimates from BrONO2 measurements during two
balloon flights of the MIPAS-B instrument (21.6 and 22.7 pptv, respectively) agree with both mid-latitude and tropical MIPAS/Envisat values.
Conclusions
We have presented the first global dataset of BrONO2 volume mixing
ratio profiles for day and night derived from 10∘-zonally and 3-daily
averaged MIPAS spectra covering the whole period of observations from
2002 to 2012. A comparison with EMAC model simulations confirms overall our current understanding of the chemical processes influencing the global zonal
mean distribution as well as the diurnal and seasonal variations of BrONO2 in the stratosphere. Still, remaining differences indicate
uncertainties in modelled processes as well as in boundary conditions.
One deviation, the underestimation of BrONO2 concentrations by the
model at high latitudes during winter, could be explained. It is caused by the
missing additional NOx source in the model located in the mesosphere
and lower thermosphere. The energetic-particle-produced NOx is transported downwards by polar winter subsidence, thereby contributing to the
production of BrONO2 – a process which we could observe here for the
first time. In future, modelling efforts are envisaged to study this effect on
the high-latitude bromine budget as well as its impact on stratospheric ozone.
Two further inconsistencies between model and measurement are more difficult
to unravel, and final explanations remain open. First, a globally present disparity is the higher simulated values in the lower stratosphere, especially
at night. Sensitivity calculations with our 1D model indicate as the only
possible means to decrease BrONO2 concentrations a more efficient
heterogeneous loss of BrONO2, e.g. via Reaction (). However, to reach values compatible with the
observations, an increase in aerosol surface area densities and/or reaction probabilities would be required. Even for reaction probabilities of unity,
aerosol SADs would have to be increased by factors of 2–3 to reproduce the
observations of BrONO2. Such an increase in lower stratospheric
aerosol SAD would, however, not agree with current satellite and in situ observations. Another possible cause, an overly efficient conversion of
organic to inorganic bromine species in the model, would be in disagreement
with previous balloon and satellite observation of BrO.
Further, the model showed an underestimation of BrONO2 abundances at
low latitudes and altitudes of around 27–32 km during day time. Here, only an increase in the production of BrONO2
(Reaction ) within its uncertainty range led to sufficient agreement with the observations at altitudes around 30 km, albeit
aggravating it below about 27 km. Inclusion of Reaction (),
the depletion of BrONO2 via reaction with O(3P), only increases the difference between model and simulation. It should be noted here that
independent information on the reaction parameters for Reaction () is missing (Burkholder et al., 2019), which might raise concern about its validity.
While we cannot rule out for sure that unaccounted systematic errors in the
observations are responsible for these discrepancies, this seems rather
unlikely given their overall fit to the model as well as the error
assessment. This view is supported by the estimation of the total
stratospheric bromine content from MIPAS BrONO2 measurements for years
of stratospheric entry between 1997 and 2007, i.e. around the maximum of
stratospheric total bromine content . At mid latitudes, where the model correction to estimate Bry from observed BrONO2 volume mixing ratios is smallest, we derived an average value
of 21.2±1.4pptv of total stratospheric Bry, which fits very well to independent estimates based on observations of BrO and to
Bry estimates derived from BrONO2 observations of the MIPAS
balloon experiment.
If it is the case that the inconsistencies between model and observations as
discussed above are due to model uncertainties, these inconsistencies could
also affect estimated ozone loss processes through bromine cycles. In future,
our dataset of BrONO2 from MIPAS can be combined e.g. with the
simultaneous day-time BrO observations from the SCIAMACHY instrument on Envisat to investigate the revealed issues about possible deficiencies in our
understanding of stratospheric bromine chemistry as well as to gain more
insight into possible uncertainties in the observations.
Application of averaging kernels to EMAC model results
The EMAC model data without and with application of the respective averaging kernels from the BrONO2 retrieval are shown in Figs. and .
Same as in Fig. but showing the pure model results of BrONO2 volume mixing ratios in dark conditions on the left-hand side in comparison to the model results with the retrieval averaging kernels applied on the right-hand side.
Figures and show the results of NO2 volume mixing ratio profiles simultaneously retrieved with BrONO2 in comparison with EMAC model data.
Bimonthly averaged cross sections (altitude versus latitude) of NO2 volume mixing ratios for dark conditions. Left: measurements, middle: model with retrieval averaging kernels applied, right: model minus MIPAS.
MIPAS level-1b data are provided by ESA (https://earth.esa.int/web/sppa/mission-performance/esa-missions/envisat/mipas/products-availability/level-1/level1-8.03, last access: 14 December 2021; ).
SAGE II data were obtained from the NASA Langley Research Center Atmospheric Science Data Center https://asdc.larc.nasa.gov/project/SAGE%20II/SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0 (last access: 14 December 2021; ). In situ aerosol data were retrieved from http://www-das.uwyo.edu/~deshler/Data/Aer_Meas_Wy_read_me.htm (last access: 14 December 2021; ). The MIPAS BrONO2 dataset and model results are available upon request from the author and at the KITopen repository, 10.5445/IR/1000136324. The MIPAS age-of-air dataset is available upon request from Gabriele Stiller (gabriele.stiller@kit.edu).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-18433-2021-supplement.
Author contributions
MH performed the MIPAS data retrieval with input from GW, GS and TvC. JO advised on spectroscopy. OK, RR, BMS, GW, SJ and MH performed and supported simulations with EMAC and the 1D model. FH and GS provided age-of-air data from MIPAS. All the authors contributed to the scientific discussion. MH prepared the manuscript with support from all the co-authors.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “IMK–IAA MIPAS version 8 data: retrieval, validation, and application (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Provision of MIPAS level-1b calibrated spectra by ESA and meteorological analysis data by ECMWF is acknowledged.
SAGE II satellite and in situ balloon data on aerosol surface area density were obtained from the NASA Langley Research Center Atmospheric Science Data Center and the University of Wyoming, Department of Atmospheric Science (Terry Deshler), respectively.
We would like to thank Klaus Pfeilsticker (University of Heidelberg) and Francois Hendrick (Belgian Institute for Space Aeronomy) for providing data on Bry from Fig. 1-16 in . The EMAC simulations were performed on the supercomputer ForHLR funded by the Ministry of Science, Research and the Arts Baden-Württemberg and by the German Federal Ministry of Education and Research.
We were supported by the German Federal Ministry of Education and Research through the project “Surface Climate Impacts of Halogen Induced Stratospheric Ozone Changes (SCI-HI)”, grant 01LG1908A, as part of the programme ROMIC-II (“Role of the Middle Atmosphere in Climate”).
Financial support
This research has been supported by the Bundesministerium für Bildung und Forschung (grant no. 01LG1908A).The article processing charges for this open-access publication were covered by the Karlsruhe Institute of Technology (KIT).
Review statement
This paper was edited by Michel Van Roozendael and reviewed by Rafael Pedro Fernandez and one anonymous referee.
ReferencesAschmann, J. and Sinnhuber, B.-M.: Contribution of very short-lived substances to stratospheric bromine loading: uncertainties and constraints, Atmos. Chem. Phys., 13, 1203–1219, 10.5194/acp-13-1203-2013, 2013.Atkinson, R., Baulch, D. L., Cox, R. A., Crowley, J. N., Hampson, R. F., Hynes, R. G., Jenkin, M. E., Rossi, M. J., and Troe, J.: Evaluated kinetic and photochemical data for atmospheric chemistry: Volume III – gas phase reactions of inorganic halogens, Atmos. Chem. Phys., 7, 981–1191, 10.5194/acp-7-981-2007, 2007.Burkholder, J. B., Ravishankara, A. R., and Solomon, S.: UV/visible and IR absorption cross sections of BrONO2, J. Geophys. Res.-Atmos., 100, 16793, 10.1029/95JD01223, 1995.Burkholder, J. B., Sander, S. P., Abbatt, J., Barker, J. R., Huie, R. E.,
Kolb, C. E., Kurylo, M. J., Orkin, V. L., Wilmouth, D. M., and Wine, P. H.:
Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies:
Evaluation No. 18, vol. 15-10 of JPL Publications, Jet Propulsion
Laboratory, Pasadena, available at: http://jpldataeval.jpl.nasa.gov (last access: 14 December 2021), 2015.Burkholder, J. B., Sander, S. P., Abbatt, J., Barker, J. R., Cappa, C.,
Crounse, J. D., Dibble, T. S., Huie, R. E., Kolb, C. E., Kurylo, M. J.,
Orkin, V. L., Percival, C. J., Wilmouth, D. M., and Wine, P. H.: Chemical
Kinetics and Photochemical Data for Use in Atmospheric Studies: Evaluation
No. 19, vol. 19-5 of JPL Publications, Jet Propulsion Laboratory, Pasadena,
available at: http://jpldataeval.jpl.nasa.gov (last access: 14 December 2021), 2019.Carroll, M. A., Sanders, R. W., Solomon, S., and Schmeltekopf, A. L.: Visible and near-ultraviolet spectroscopy at McMurdo Station, Antarctica: 6. Observations of BrO, J. Geophys. Res., 94, 16633, 10.1029/JD094iD14p16633, 1989.Damadeo, R. P., Zawodny, J. M., Thomason, L. W., and Iyer, N.: SAGE version 7.0 algorithm: application to SAGE II, Atmos. Meas. Tech., 6, 3539–3561, 10.5194/amt-6-3539-2013, 2013.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 10.1002/qj.828, 2011.Deshler, T.: University of Wyoming stratospheric aerosol size distributions, University of Wyoming [data set], available at: http://www-das.uwyo.edu/~deshler/Data/Aer_Meas_Wy_read_me.htm, last access: 14 December 2021.Deshler, T., Luo, B., Kovilakam, M., Peter, T., and Kalnajs, L. E.: Retrieval
of aerosol size distributions from in situ particle counter measurements:
Instrument counting efficiency and comparisons with satellite measurements,
J. Geophys. Res.-Atmos., 124, 5058–5087, 10.1029/2018JD029558, 2019.Dorf, M., Bösch, H., Butz, A., Camy-Peyret, C., Chipperfield, M. P., Engel, A., Goutail, F., Grunow, K., Hendrick, F., Hrechanyy, S., Naujokat, B., Pommereau, J.-P., Van Roozendael, M., Sioris, C., Stroh, F., Weidner, F., and Pfeilsticker, K.: Balloon-borne stratospheric BrO measurements: comparison with Envisat/SCIAMACHY BrO limb profiles, Atmos. Chem. Phys., 6, 2483–2501, 10.5194/acp-6-2483-2006, 2006a.Dorf, M., Butler, J. H., Butz, A., Camy-Peyret, C., Chipperfield, M. P., Kritten, L., Montzka, S. A., Simmes, B., Weidner, F., and Pfeilsticker, K.: Long-term observations of stratospheric bromine reveal slow down in growth, Geophys. Res. Lett., 33, 4199, 10.1029/2006GL027714, 2006b.Dorf, M., Butz, A., Camy-Peyret, C., Chipperfield, M. P., Kritten, L., and Pfeilsticker, K.: Bromine in the tropical troposphere and stratosphere as derived from balloon-borne BrO observations, Atmos. Chem. Phys., 8, 7265–7271, 10.5194/acp-8-7265-2008, 2008.
Engel, A., Rigby, M., Burkholder, J., Fernandez, R., Froidevaux, L., Hall, B. D., Hossaini, R., Saito, T., Vollmer, M. K., and Yao, B.: Update on Ozone-Depleting Substances (ODSs) and Other Gases of Interest to the Montreal Protocol, in: Scientific Assessment of Ozone Depletion: 2018, Global Ozone Research and Monitoring Project–Report No. 58, World Meteorological Organization, Geneva, Switzerland, 2018.European Space Agency: MIPAS Level 1b IPF version 8.03, European Space Agency [data set], available at: https://earth.esa.int/web/sppa/mission-performance/esa-missions/envisat/mipas/products-availability/level-1/level1-8.03, last access: 14 December 2021.Eyring, V., Lamarque, J.-F., Hess, P., Arfeuille, F., Bowman, K.,
Chipperfield, M. P., Duncan, B., Fiore, A., Gettelman, A., Giorgetta, M. A.,
Granier, C., Hegglin, M., Kinnison, D., Kunze, M., Langematz, U., Luo, B.,
Martin, R., Matthes, K., Newman, P. A., Peter, T., Robock, A., Ryerson, A.,
Saiz-Lopez, A., Salawitch, R., Schultz, M., Shepherd, T. G., Shindell, D.,
Stähelin, J., Tegtmeier, S., Thomason, L., Tilmes, S., Vernier, J.-P.,
Waugh, D. W., and Young, P.: Overview of IGAC/SPARC Chemistry-Climate Model
Initiative (CCMI) Community Simulations in Support of Upcoming Ozone and
Climate Assessments, SPARC Newsletter, 48–66, available at: http://www.sparc-climate.org/fileadmin/customer/6_Publications/Newsletter_PDF/40_SPARCnewsletter_Jan2013_web.pdf (last access: 14 December 2021), 2013.Falk, S., Sinnhuber, B.-M., Krysztofiak, G., Jöckel, P., Graf, P., and Lennartz, S. T.: Brominated VSLS and their influence on ozone under a changing climate, Atmos. Chem. Phys., 17, 11313–11329, 10.5194/acp-17-11313-2017, 2017.Fischer, H., Birk, M., Blom, C., Carli, B., Carlotti, M., von Clarmann, T., Delbouille, L., Dudhia, A., Ehhalt, D., Endemann, M., Flaud, J. M., Gessner, R., Kleinert, A., Koopman, R., Langen, J., López-Puertas, M., Mosner, P., Nett, H., Oelhaf, H., Perron, G., Remedios, J., Ridolfi, M., Stiller, G., and Zander, R.: MIPAS: an instrument for atmospheric and climate research, Atmos. Chem. Phys., 8, 2151–2188, 10.5194/acp-8-2151-2008, 2008.Fish, D. J., Jones, R. L., and Strong, E. K.: Midlatitude observations of the diurnal variation of stratospheric BrO, J. Geophys. Res.-Atmos., 100, 18863, 10.1029/95JD01944, 1995.Friedl, R. R., May, R. D., and Duxbury, G.: The ν6, ν7, ν8, and ν10 Bands of HO2NO2, J. Mol. Spectrosc., 165, 481–493, 10.1006/jmsp.1994.1151, 1994.Funke, B., López-Puertas, M., Gil-López, S., von Clarmann, T., Stiller, G. P., Fischer, H., and Kellmann, S.: Downward transport of upper atmospheric NOx into the polar stratosphere and lower mesosphere during the Antarctic 2003 and Arctic 2002/2003 winters, J. Geophys. Res.-Atmos., 110, D24308, 10.1029/2005JD006463, 2005.Funke, B., López-Puertas, M., Stiller, G. P., and von Clarmann, T.: Mesospheric and stratospheric NOy produced by energetic particle precipitation during 2002–2012, J. Geophys. Res.-Atmos., 119, 4429–4446, 10.1002/2013JD021404, 2014.Gordon, I. E., Rothman, L. S., Hill, C., Kochanov, R. V., Tan, Y., Bernath, P. F., Birk, M., Boudon, V., Campargue, A., Chance, K. V., Drouin, B. J., Flaud, J.-M., Gamache, R. R., Hodges, J. T., Jacquemart, D., Perevalov, V. I., Perrin, A., Shine, K. P., Smith, M.-A., Tennyson, J., Toon, G. C., Tran, H., Tyuterev, V. G., Barbe, A., Császár, A. G., Devi, V. M., Furtenbacher, T., Harrison, J. J., Hartmann, J.-M., Jolly, A., Johnson, T. J., Karman, T., Kleiner, I., Kyuberis, A. A., Loos, J., Lyulin, O. M., Massie, S. T., Mikhailenko, S. N., Moazzen-Ahmadi, N., Müller, H., Naumenko, O. V., Nikitin, A. V., Polyansky, O. L., Rey, M., Rotger, M., Sharpe, S. W., Sung, K., Starikova, E., Tashkun, S. A., Auwera, J. V., Wagner, G., Wilzewski, J., Wcisło, P., Yu, S., and Zak, E. J.: The HITRAN2016 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 203, 3–69, 10.1016/j.jqsrt.2017.06.038, 2017.Günther, A., Höpfner, M., Sinnhuber, B.-M., Griessbach, S., Deshler, T., von Clarmann, T., and Stiller, G.: MIPAS observations of volcanic sulfate aerosol and sulfur dioxide in the stratosphere, Atmos. Chem. Phys., 18, 1217–1239, 10.5194/acp-18-1217-2018, 2018.Haenel, F. J., Stiller, G. P., von Clarmann, T., Funke, B., Eckert, E., Glatthor, N., Grabowski, U., Kellmann, S., Kiefer, M., Linden, A., and Reddmann, T.: Reassessment of MIPAS age of air trends and variability, Atmos. Chem. Phys., 15, 13161–13176, 10.5194/acp-15-13161-2015, 2015.Hanson, D. R.: Reactivity of BrONO2 and HOBr on sulfuric acid solutions at low temperatures, J. Geophys. Res., 108, 14009, 10.1029/2002JD002519, 2003.Hanson, D. R., Ravishankara, A. R., and Lovejoy, E. R.: Reaction of BrONO2 with H2O on submicron sulfuric acid aerosol and the implications for the lower stratosphere, J. Geophys. Res.-Atmos., 101, 9063–9069, 10.1029/96JD00347, 1996.Harder, H., Bösch, H., Camy-Peyret, C., Chipperfield, M. P., Fitzenberger, R., Payan, S., Perner, D., Platt, U., Sinnhuber, B.-M., and Pfeilsticker, K.: Comparison of measured and modeled stratospheric BrO: Implications for the total amount of stratospheric bromine, Geophys. Res. Lett., 27, 3695–3698, 10.1029/1999GL011215, 2000.Hendrick, F., Johnston, P. V., de Mazière, M., Fayt, C., Hermans, C., Kreher, K., Theys, N., Thomas, A., and van Roozendael, M.: One-decade trend analysis of stratospheric BrO over Harestua (60∘ N) and Lauder (45∘ S) reveals a decline, Geophys. Res. Lett., 35, 1909, 10.1029/2008GL034154, 2008.Höpfner, M., Orphal, J., von Clarmann, T., Stiller, G., and Fischer, H.: Stratospheric BrONO2 observed by MIPAS, Atmos. Chem. Phys., 9, 1735–1746, 10.5194/acp-9-1735-2009, 2009.Höpfner, M., Glatthor, N., Grabowski, U., Kellmann, S., Kiefer, M., Linden, A., Orphal, J., Stiller, G., von Clarmann, T., Funke, B., and Boone, C. D.: Sulfur dioxide (SO2) as observed by MIPAS/Envisat: temporal development and spatial distribution at 15–45 km altitude, Atmos. Chem. Phys., 13, 10405–10423, 10.5194/acp-13-10405-2013, 2013.Höpfner, M., Volkamer, R., Grabowski, U., Grutter, M., Orphal, J., Stiller, G., von Clarmann, T., and Wetzel, G.: First detection of ammonia (NH3) in the Asian summer monsoon upper troposphere, Atmos. Chem. Phys., 16, 14357–14369, 10.5194/acp-16-14357-2016, 2016.Höpfner, M., Kirner, O., Wetzel, G., Sinnhuber, B.-M., Haenel, F.,
Johansson, S., Orphal, J., Ruhnke, R., Stiller, G., and von Clarmann, T.: The
MIPAS global climatology of BrONO2 2002–2012 in comparison to EMAC
model data, KIT [data set], 10.5445/IR/1000136324, 2021.Ingham, T., Bauer, D., Landgraf, J., and Crowley, J. N.: Ultraviolet-Visible Absorption Cross Sections of Gaseous HOBr, J. Phys. Chem. A, 102, 3293–3298, 10.1021/jp980272c, 1998.Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717–752, 10.5194/gmd-3-717-2010, 2010.Jöckel, P., Tost, H., Pozzer, A., Kunze, M., Kirner, O., Brenninkmeijer, C. A. M., Brinkop, S., Cai, D. S., Dyroff, C., Eckstein, J., Frank, F., Garny, H., Gottschaldt, K.-D., Graf, P., Grewe, V., Kerkweg, A., Kern, B., Matthes, S., Mertens, M., Meul, S., Neumaier, M., Nützel, M., Oberländer-Hayn, S., Ruhnke, R., Runde, T., Sander, R., Scharffe, D., and Zahn, A.: Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51, Geosci. Model Dev., 9, 1153–1200, 10.5194/gmd-9-1153-2016, 2016.Keber, T., Bönisch, H., Hartick, C., Hauck, M., Lefrancois, F., Obersteiner, F., Ringsdorf, A., Schohl, N., Schuck, T., Hossaini, R., Graf, P., Jöckel, P., and Engel, A.: Bromine from short-lived source gases in the extratropical northern hemispheric upper troposphere and lower stratosphere (UTLS), Atmos. Chem. Phys., 20, 4105–4132, 10.5194/acp-20-4105-2020, 2020.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.Klobas, J. E., Weisenstein, D. K., Salawitch, R. J., and Wilmouth, D. M.: Reformulating the bromine alpha factor and equivalent effective stratospheric chlorine (EESC): evolution of ozone destruction rates of bromine and chlorine in future climate scenarios, Atmos. Chem. Phys., 20, 9459–9471, 10.5194/acp-20-9459-2020, 2020.Koenig, T. K., Volkamer, R., Baidar, S., Dix, B., Wang, S., Anderson, D. C., Salawitch, R. J., Wales, P. A., Cuevas, C. A., Fernandez, R. P., Saiz-Lopez, A., Evans, M. J., Sherwen, T., Jacob, D. J., Schmidt, J., Kinnison, D., Lamarque, J.-F., Apel, E. C., Bresch, J. C., Campos, T., Flocke, F. M., Hall, S. R., Honomichl, S. B., Hornbrook, R., Jensen, J. B., Lueb, R., Montzka, D. D., Pan, L. L., Reeves, J. M., Schauffler, S. M., Ullmann, K., Weinheimer, A. J., Atlas, E. L., Donets, V., Navarro, M. A., Riemer, D., Blake, N. J., Chen, D., Huey, L. G., Tanner, D. J., Hanisco, T. F., and Wolfe, G. M.: BrO and inferred Bry profiles over the western Pacific: relevance of inorganic bromine sources and a Bry minimum in the aged tropical tropopause layer, Atmos. Chem. Phys., 17, 15245–15270, 10.5194/acp-17-15245-2017, 2017.Kovalenko, L. J., Livesey, N. L., Salawitch, R. J., Camy-Peyret, C., Chipperfield, M. P., Cofield, R. E., Dorf, M., Drouin, B. J., Froidevaux, L., Fuller, R. A., Goutail, F., Jarnot, R. F., Jucks, K., Knosp, B. W., Lambert, A., MacKenzie, I. A., Pfeilsticker, K., Pommereau, J.-P., Read, W. G., Santee, M. L., Schwartz, M. J., Snyder, W. V., Stachnik, R., Stek, P. C., Wagner, P. A., and Waters, J. W.: Validation of Aura Microwave Limb Sounder BrO observations in the stratosphere, J. Geophys. Res., 112, 6154, 10.1029/2007JD008817, 2007.Kreycy, S., Camy-Peyret, C., Chipperfield, M. P., Dorf, M., Feng, W., Hossaini, R., Kritten, L., Werner, B., and Pfeilsticker, K.: Atmospheric test of the J(BrONO2)/kBrO+NO2 ratio: implications for total stratospheric Bry and bromine-mediated ozone loss, Atmos. Chem. Phys., 13, 6263–6274, 10.5194/acp-13-6263-2013, 2013.Lary, D. J.: Gas phase atmospheric bromine photochemistry, J. Geophys. Res.-Atmos., 101, 1505–1516, 10.1029/95JD02463, 1996.Lary, D. J.: Catalytic destruction of stratospheric ozone, J. Geophys. Res.-Atmos., 102, 21515–21526, 10.1029/97JD00912, 1997.Livesey, N. J., Kovalenko, L. J., Salawitch, R. J., MacKenzie, I. A., Chipperfield, M. P., Read, W. G., Jarnot, R. F., and Waters, J. W.: EOS Microwave Limb Sounder observations of upper stratospheric BrO: Implications for total bromine, Geophys. Res. Lett., 33, 1781, 10.1029/2006GL026930, 2006.May, R. D. and Friedl, R. R.: Integrated band intensities of HO2NO2 at 220 K, J. Quant. Spectrosc. Ra., 50, 257–266, 10.1016/0022-4073(93)90076-T, 1993.McLinden, C. A., Haley, C. S., Lloyd, N. D., Hendrick, F., Rozanov, A., Sinnhuber, B.-M., Goutail, F., Degenstein, D. A., Llewellyn, E. J., Sioris, C. E., van Roozendael, M., Pommereau, J. P., Lotz, W., and Burrows, J. P.: Odin/OSIRIS observations of stratospheric BrO: Retrieval methodology, climatology, and inferred Bry, J. Geophys. Res., 115, 4199, 10.1029/2009JD012488, 2010.Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, 10.1007/s10584-011-0156-z, 2011.Millán, L., Livesey, N., Read, W., Froidevaux, L., Kinnison, D., Harwood, R., MacKenzie, I. A., and Chipperfield, M. P.: New Aura Microwave Limb Sounder observations of BrO and implications for Bry, Atmos. Meas. Tech., 5, 1741–1751, 10.5194/amt-5-1741-2012, 2012.NASA Langley Research Center: Stratospheric Aerosol and Gas Experiment (SAGE) II Version 7.0 Aerosol, O3, NO2 and H2O Profiles in binary format, NASA Langley Research Center [data set], available at: https://asdc.larc.nasa.gov/project/SAGE%20II/SAGE2_AEROSOL_O3_NO2_H2O_BINARY_V7.0, last access: 14 December 2021.Parrella, J. P., Chance, K., Salawitch, R. J., Canty, T., Dorf, M., and Pfeilsticker, K.: New retrieval of BrO from SCIAMACHY limb: an estimate of the stratospheric bromine loading during April 2008, Atmos. Meas. Tech., 6, 2549–2561, 10.5194/amt-6-2549-2013, 2013.Pundt, I.: Climatology of the stratospheric BrO vertical distribution by balloon-borne UV–visible spectrometry, J. Geophys. Res., 107, 1195, 10.1029/2002JD002230, 2002.Remedios, J. J., Leigh, R. J., Waterfall, A. M., Moore, D. P., Sembhi, H., Parkes, I., Greenhough, J., Chipperfield, M. P., and Hauglustaine, D.: MIPAS reference atmospheres and comparisons to V4.61/V4.62 MIPAS level 2 geophysical data sets, Atmos. Chem. Phys. Discuss., 7, 9973–10017, 10.5194/acpd-7-9973-2007, 2007.
Rodgers, C. D.: Inverse methods for atmospheric sounding: Theory and practice, vol. 2 of Series on atmospheric oceanic and planetary physics, World Scientific, Singapore, reprinted. edn., 2004.Roeckner, E., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kornblueh, L., Manzini, E., Schlese, U., and Schulzweida, U.: Sensitivity of Simulated Climate to Horizontal and Vertical Resolution in the ECHAM5 Atmosphere Model, J. Climate, 19, 3771–3791, 10.1175/JCLI3824.1, 2006.Rotermund, M. K., Bense, V., Chipperfield, M. P., Engel, A., Grooß, J.-U., Hoor, P., Hüneke, T., Keber, T., Kluge, F., Schreiner, B., Schuck, T., Vogel, B., Zahn, A., and Pfeilsticker, K.: Organic and inorganic bromine measurements around the extratropical tropopause and lowermost stratosphere: insights into the transport pathways and total bromine, Atmos. Chem. Phys., 21, 15375–15407, 10.5194/acp-21-15375-2021, 2021.Rozanov, A., Kühl, S., Doicu, A., McLinden, C., Puķīte, J., Bovensmann, H., Burrows, J. P., Deutschmann, T., Dorf, M., Goutail, F., Grunow, K., Hendrick, F., von Hobe, M., Hrechanyy, S., Lichtenberg, G., Pfeilsticker, K., Pommereau, J. P., Van Roozendael, M., Stroh, F., and Wagner, T.: BrO vertical distributions from SCIAMACHY limb measurements: comparison of algorithms and retrieval results, Atmos. Meas. Tech., 4, 1319–1359, 10.5194/amt-4-1319-2011, 2011.Sander, R., Kerkweg, A., Jöckel, P., and Lelieveld, J.: Technical note: The new comprehensive atmospheric chemistry module MECCA, Atmos. Chem. Phys., 5, 445–450, 10.5194/acp-5-445-2005, 2005.Sander, S. P., Abbatt, J., Barker, J. R., Burkholder, J. B., Friedl, R. R.,
Golden, D. M., Huie, R. E., Kolb, C. E., Kurylo, M. J., Moortgat, G. K.,
Orkin, V. L., and Wine, P. H.: Chemical Kinetics and Photochemical Data for
Use in Atmospheric Studies: Evaluation No. 17, vol. 10-6 of JPL
Publications, Jet Propulsion Laboratory, Pasadena, available at: http://jpldataeval.jpl.nasa.gov (last access: 14 December 2021), 2011.Sinnhuber, B.-M., Rozanov, A., Sheode, N., Afe, O. T., Richter, A., Sinnhuber, M., Wittrock, F., and Burrows, J. P.: Global observations of stratospheric bromine monoxide from SCIAMACHY, Geophys. Res. Lett., 32, 831, 10.1029/2005GL023839, 2005.Sinnhuber, B.-M., Sheode, N., Sinnhuber, M., Chipperfield, M. P., and Feng, W.: The contribution of anthropogenic bromine emissions to past stratospheric ozone trends: a modelling study, Atmos. Chem. Phys., 9, 2863–2871, 10.5194/acp-9-2863-2009, 2009.Sioris, C. E., Kovalenko, L. J., McLinden, C. A., Salawitch, R. J., van Roozendael, M., Goutail, F., Dorf, M., Pfeilsticker, K., Chance, K., von Savigny, C., Liu, X., Kurosu, T. P., Pommereau, J.-P., Bösch, H., and Frerick, J.: Latitudinal and vertical distribution of bromine monoxide in the lower stratosphere from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography limb scattering measurements, J. Geophys. Res., 111, D14301, 10.1029/2005JD006479, 2006.Soller, R., Nicovich, J. M., and Wine, P. H.: Temperature-Dependent Rate Coefficients for the Reactions of Br(2P3/2), Cl(2P3/2), and O(3PJ) with BrONO2, J. Phys. Chem. A, 105, 1416–1422, 10.1021/jp001947q, 2001.Solomon, S., Sanders, R. W., Carroll, M. A., and Schmeltekopf, A. L.: Visible and near-ultraviolet spectroscopy at McMurdo Station, Antarctica: 5. Observations of the diurnal variations of BrO and OClO, J. Geophys. Res.-Atmos., 94, 11393, 10.1029/JD094iD09p11393, 1989.Spang, R., Remedios, J., and Barkley, M.: Colour indices for the detection and differentiation of cloud types in infra-red limb emission spectra, Adv. Space Res., 33, 1041–1047, 10.1016/S0273-1177(03)00585-4, 2004.Spencer, J. E. and
Rowland, F. S.: Bromine nitrate and its stratospheric significance, J. Phys. Chem., 82, 7–10, 10.1021/j100490a002, 1978.Stachnik, R. A., Millán, L., Jarnot, R., Monroe, R., McLinden, C., Kühl, S., Puķīte, J., Shiotani, M., Suzuki, M., Kasai, Y., Goutail, F., Pommereau, J. P., Dorf, M., and Pfeilsticker, K.: Stratospheric BrO abundance measured by a balloon-borne submillimeterwave radiometer, Atmos. Chem. Phys., 13, 3307–3319, 10.5194/acp-13-3307-2013, 2013.Steck, T.: Methods for determining regularization for atmospheric retrieval problems, Appl. Optics, 41, 1788–1797, 10.1364/ao.41.001788, 2002.Stiller, G. P., Harrison, J. J., Haenel, F. J., Glatthor, N., Kellmann, S., and von Clarmann, T.: Improved global distributions of SF6 and mean age of stratospheric air by use of new spectroscopic data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2660, 10.5194/egusphere-egu2020-2660, 2020.Theys, N., Van Roozendael, M., Hendrick, F., Fayt, C., Hermans, C., Baray, J.-L., Goutail, F., Pommereau, J.-P., and De Mazière, M.: Retrieval of stratospheric and tropospheric BrO columns from multi-axis DOAS measurements at Reunion Island (21∘ S, 56∘ E), Atmos. Chem. Phys., 7, 4733–4749, 10.5194/acp-7-4733-2007, 2007.Theys, N., Van Roozendael, M., Errera, Q., Hendrick, F., Daerden, F., Chabrillat, S., Dorf, M., Pfeilsticker, K., Rozanov, A., Lotz, W., Burrows, J. P., Lambert, J.-C., Goutail, F., Roscoe, H. K., and De Mazière, M.: A global stratospheric bromine monoxide climatology based on the BASCOE chemical transport model, Atmos. Chem. Phys., 9, 831–848, 10.5194/acp-9-831-2009, 2009.
Tikhonov, A. N.: On the solution of ill-posed problems and the method of regularization, Dokl. Akad. Nauk SSSR, 151, 501–504, 1963.
van Aalst, M. K.: Dynamics and transport in the stratosphere: Simulations with a general circulation model, Dissertation, Utrecht University, Utrecht, 2005.Wagner, G. and Birk, M.: New infrared spectroscopic database for bromine nitrate, J. Mol. Spectrosc., 326, 95–105, 10.1016/j.jms.2016.03.007, 2016.
Warwick, N. J., Pyle, J. A., Carver, G. D., Yang, X., Savage, N. H., O'Connor, F. M., and Cox, R. A.: Global modeling of biogenic bromocarbons, J. Geophys. Res.-Atmos., 111, D24305, 10.1029/2006JD007264, 2006.Werner, B., Stutz, J., Spolaor, M., Scalone, L., Raecke, R., Festa, J., Colosimo, S. F., Cheung, R., Tsai, C., Hossaini, R., Chipperfield, M. P., Taverna, G. S., Feng, W., Elkins, J. W., Fahey, D. W., Gao, R.-S., Hintsa, E. J., Thornberry, T. D., Moore, F. L., Navarro, M. A., Atlas, E., Daube, B. C., Pittman, J., Wofsy, S., and Pfeilsticker, K.: Probing the subtropical lowermost stratosphere and the tropical upper troposphere and tropopause layer for inorganic bromine, Atmos. Chem. Phys., 17, 1161–1186, 10.5194/acp-17-1161-2017, 2017.Wetzel, G., Oelhaf, H., Höpfner, M., Friedl-Vallon, F., Ebersoldt, A., Gulde, T., Kazarski, S., Kirner, O., Kleinert, A., Maucher, G., Nordmeyer, H., Orphal, J., Ruhnke, R., and Sinnhuber, B.-M.: Diurnal variations of BrONO2 observed by MIPAS-B at midlatitudes and in the Arctic, Atmos. Chem. Phys., 17, 14631–14643, 10.5194/acp-17-14631-2017, 2017.
WMO: Scientific assessment of ozone depletion: 2018, Global Ozone Research and Monitoring Project–Report No. 58, World Meteorological Organization, Geneva, Switzerland, 2018.Wofsy, S. C., McElroy, M. B., and Yung, Y. L.: The chemistry of atmospheric bromine, Geophys. Res. Lett., 2, 215–218, 10.1029/GL002i006p00215, 1975.