Volcanic eruptions can increase the stratospheric sulfur loading by orders
of magnitude above the background level and are the most important source of
variability in stratospheric sulfur. We present a set of vertical profiles
of sulfate aerosol volume densities and derived liquid-phase H
Aerosol particles are omnipresent in the atmosphere and can affect climate,
air quality, and atmospheric chemistry. In the stratosphere, aerosol
particles are mainly composed of sulfuric acid (H
The main source gases of stratospheric sulfate aerosol during
background/non-volcanic conditions are sulfur dioxide (SO
When analysing the vertical extent of SO
In Sect. 2 we first provide basic information on MIPAS, the MIPAS SO
MIPAS (Fischer et al., 2008) is an infrared (IR) limb emission sounder that
was operated on ESAs (European Space Agency) satellite Envisat. The Fourier
transform spectrometer measured high-resolution spectra emitted by the
constituents of the atmosphere in the thermal IR, in the region 685 to
2410 cm
In this study we use the MIPAS SO
To validate the new MIPAS aerosol data set described in Sect. 3, we use
aerosol volume density profiles that were derived from in situ measurements
of stratospheric aerosol above Laramie, Wyoming (41
The chemical transport model (CTM) used in our study (e.g. Sinnhuber et al.,
2003; Kiesewetter et al., 2010) is forced by temperature, wind fields, and
diabatic heating rates from ERA-Interim (Dee et al., 2011). The model uses
isentropes as vertical coordinates. Horizontal transport at levels of
constant potential temperature is derived from the wind fields, while
vertical transport is calculated using the diabatic heating rates. The CTM
employs the second-order moments advection scheme by Prather (1986). The
model domain covers 29 isentropic levels between 330 and 2700 K
(
As part of this study, a sulfur module has been implemented, including OCS,
SO
The model is run for 365 d per simulation, with a time step of 30 min and
tracer fields are written out daily at 12:00 UTC. For the eruption of
Kasatochi (7 August 2008) the individual runs are started on the
31 July 2008, and for the eruption of Sarychev (12 June 2009) all runs are
started on the 31 May 2009. As the relevant initial trace gas fields are set
to zero and the model is driven by ERA-Interim data, which are updated every
6 h, no long spin-up time is needed. Per volcano four simulations were made
that differ concerning the particle size of sulfate aerosol. Simulations were
made with constant radii of 0.1, 0.5, and 1
Imaginary parts of refractive indices for aqueous H
In previous analyses of mid-infrared observations by MIPAS-B (the
balloon-borne predecessor of the MIPAS satellite instrument; Friedl-Vallon et
al., 2004) and MIPAS/Envisat (MIPAS instrument on the satellite Envisat,
generally referred to as “MIPAS” throughout the present work), it has been
demonstrated that the limb radiances due to particles have two major
contributing terms: (1) the thermal emission of the particles and (2) the
scattered radiation from the atmosphere and Earth's surface from below the
tangent point (Höpfner et al., 2002, 2006). The relative weights of these
contributions differ with particle size and wavenumber. For particles
sufficiently small compared to the wavelength
(
For this study we have concentrated on the second MIPAS measurement period
between January 2005 and April 2012. The retrieval model used is the
KOPRA–KOPRAFIT (Karlsruhe
Optimized and Precise Radiative transfer Algorithm) suite, allowing us to
directly retrieve aerosol parameters from observed radiances by coupling a
Mie model with the line-by-line radiative transfer scheme (Stiller et al.,
2002; Höpfner et al., 2002, 2006). For aerosol composition we assume a
75 % by weight (75 wt %) H
Altitude-dependent estimated errors for the retrieval of
H
The retrieval has been set up as a multiparameter nonlinear least-squares fit
of the calculated to the measured limb radiances of entire limb scans (e.g.
von Clarmann et al., 2003). Besides the target parameter, namely sulfate
aerosol volume densities, further atmospheric fit parameters of the retrieval
are the vertical profiles of spectrally interfering trace gases methane
(CH
An overview of the leading error components is presented in Fig. 2, with the
assumed parameter uncertainties listed in the caption. The error
contributions are estimated from a subset of a few hundred single cases by
sensitivity studies using perturbed parameters or, in the case of spectral noise,
directly from the retrieval diagnostics. The total error changes with
altitude from around 20 % (0.09
Prior to the retrieval, a deselection of spectra affected by clouds was
performed via the application of an established cloud filter method for MIPAS
by Spang et al. (2004). To sort out optically thick clouds, but not all
aerosol-affected spectra, this cloud filter was applied with a cloud index
limit of 1.7. Due to this loose setting of the cloud-filter, artefacts
caused, e.g. by thin cirrus clouds, polar stratospheric clouds (PSCs), or
volcanic ash remain in the data set and are all attributed to the retrieved
75 wt % H
Profiles of aerosol volume densities from in situ and MIPAS
satellite measurements. In situ data are balloon-borne data above the lapse
rate tropopause for Laramie, Wyoming (41
Two distinct features of strong enhancements with an annual cycle show up in
the unfiltered data set. The first feature is due to strong enhancements in
the presence of PSCs at the winter pole. To deselect PSC-affected profiles, a
filter is applied when temperatures in the altitude range 17–23 km drop
below a threshold of 195 K polewards of 40
The second feature is assumed to be induced by thin cirrus clouds. It is
present mainly in the tropics, at around 25
To validate the new data set, we compare the profiles of MIPAS aerosol volume
density to in situ balloon measurements (Deshler et al., 2003). In situ
measurements were carried out with laser-based aerosol spectrometers from
Laramie, Wyoming (41
Aerosol volume densities by MIPAS and in situ data from Laramie,
Wyoming.
Generally, the aerosol volume densities (Figs. 3 and 4) are highest in the
lower stratosphere and then decrease towards zero at higher altitudes. As the
balloon data have a higher vertical resolution and the retrieval process for
MIPAS profiles includes smoothing, the in situ data show finer structures.
Compared to the balloon data, the original MIPAS aerosol volume densities
show a positive bias in most profiles (Fig. 3) as well as in the mean profile
(Fig. 4). This is most easily detectable at higher altitudes where profiles
are relatively smooth. The offset amplifies towards lower altitudes
(Fig. 4b). Aiming at a reduction in this positive offset, a height-dependent
de-biasing is performed on all single MIPAS profiles. The de-biasing is based
on the in situ measurements carried out with laser-based particle counters.
MIPAS profiles show a consistent variation with height, compared to the LPC
measurements. An additive linear de-biasing is applied, rather than a
multiplicative correction, as the offset is expected to be caused by an
altitude-dependent additive stray light error in the radiances (see
Sect. 3.1). The de-biasing is based on the absolute differences between the
aerosol volume densities of the mean MIPAS and in situ profiles (Fig. 4b,
purple solid profile) at 18–30 km, where profiles show weak variability and
a relatively low uncertainty in the bias. A regression line (Fig. 4b, red
line) to the profile of absolute differences represents the vertically
resolved values of the de-biasing function, which are subtracted from each
MIPAS profile during offset correction. A narrow red shaded area indicates
the uncertainty in the bias correction and has been evaluated using
generalised Gaussian error propagation of the uncertainties of the slope and
the intercept of the regression line. No weighting of the data points by
their inverse error variances was applied in the calculation of mean in situ
and MIPAS profiles. This method was chosen in order to avoid
representativeness problems, as the error variances correlate with the
aerosol loading of the atmosphere and would thus cause a sampling artefact in
the estimated bias and offset correction. At lower altitudes where profiles
show more variability, both vertically and between the in situ and MIPAS
profile, the linear fit also works well. The uncertainty in the bias
(Fig. 4b) shows that the positive bias is not random, as the spread is rather
low and uncertainty limits are noticeably distant from zero. The mean
de-biased MIPAS profile (Fig. 4a) matches the in situ data and lies mostly in
the range of the uncertainties of the mean in situ profile. Further, the
absolute and relative differences to the balloon data are reduced
significantly (Fig. 4b and c). Percentage differences are mostly below
To study the distribution of sulfate aerosol as measured by MIPAS from 2005
to 2012, Fig. 5 (left) shows latitudinally resolved time series of
liquid-phase H
Global time series of latitudinally resolved distributions of MIPAS
liquid-phase H
The latitudinally resolved time series of sulfate aerosol mole fractions
further reveal different periodic structures, which are not connected to
volcanic activities.
In polar regions at altitudes above In both hemispheres, but primarily in the Southern Hemisphere,
mole fractions of liquid-phase H In the midlatitudes of the Northern Hemisphere, the sulfate aerosol is
increased during boreal summer at around 10–12 km. In the tropics at around 14–16 km aerosol values are elevated, while
they are very low below and above these altitudes, unless influenced by
volcanic eruptions.
As SO
Sulfur mass contained in SO
In the Northern Hemisphere at low altitudes (
We present a case study of MIPAS SO
In this section we aim to test the agreement between measured SO
In Table 1 injected SO
When comparing the SO
Volcanically emitted SO
Time series of sulfur mass contained in SO
To analyse the measured and simulated data, data sets of sulfur mass
densities (SMD: mass per unit volume) are re-sampled on a common grid
with 1 km vertical spacing and a horizontal resolution that equals the model
grid. On this new grid, the same data basis is used for the measured and
simulated data, neglecting all “grid cells” for which either only MIPAS or
only CTM data are available. For MIPAS aerosols, SMDs are calculated from the
primarily retrieved volume densities, using an assumed aerosol density of
1700 kg m
Generally, when calculating an integrated mass, high data coverage is crucial
to prevent underestimation; therefore, we use 5-day running zonal means.
Zonal mean values, used to calculate sulfur masses, are derived using a
method of increasing area averaging (see Appendix A) to reduce the bias of
mean values due to a non-uniform data coverage. Even though high data
coverage is very important, we omit available data and information, as the
same basis of available values is used for MIPAS and the CTM. This is
appropriate when analysing the agreement between the data. Data are omitted for the CTM in particular. Thus, we also provide some information on
modelled sulfur masses derived from the non-co-located data (Fig. 6a–c). The
impact of missing data is strongest in the lowermost altitude region
presented here. For MIPAS this is mainly due to the presence of clouds and
ash, which were filtered out using the cloud filter by Spang et al. (2004) in
the case of SO
To ease visual comparisons of measured and modelled sulfur mass in Fig. 6, a constant background is added to the model results, as only volcanic sulfur is considered in these simulations. The background mass is chosen considering the mass derived by MIPAS before the volcanic eruption in the region of interest per altitude and latitude bin. This does not necessarily represent normal background conditions but unmasks the anomalies caused by the volcanoes.
Concerning the measured and modelled SO
The measured decay of SO
To investigate the effect of particle sedimentation on the residence time of
sulfur after the volcanic eruptions, model simulations with different
effective sedimentation radii are performed, as well as one simulation
without any sedimentation. The radii lie in the range of aerosol size
distributions as observed by Deshler et al. (2003) and Deshler (2008) for
volcanically perturbed periods, and one constant radius is applied for all
H
We conclude from the comparisons between measured and simulated SO
SO
Further, we find that the dominating process on the evolution of volcanic
sulfur is transport by the Brewer–Dobson circulation out of the region of
interest. This becomes obvious when comparing the long-term removal of total
modelled sulfur with and without sedimentation to the observed sulfur mass
(Fig. 6d–f). In the case of the CTM, this excludes all influence by chemical
reactions on the removal of volcanic sulfur. Even though a consideration of
the sedimentation of sulfate aerosol with an effective sedimentation radius
between about 0.5 and 1
A peak can be seen in the measured and modelled sulfur dioxide and sulfuric acid masses in November/December 2008 (Fig. 6) in the lowermost altitude region (10.5–14.5 km). This peak is caused by the downward transport of sulfur in the extratropics that has been emitted by the eruption of Kasatochi. In the following section (Sect. 4.2) more details are given on this transport pattern.
In the altitude region of interest, from around 10 to 22 km height,
supplementary processes, such as the photolysis of gas-phase H
The Kasatochi eruption injected a large amount of SO
Parts of the differences between the transport patterns after the eruptions
arise from the injected SO
As Fig. 7 but for 0–30
Figure 8 shows vertically resolved measurements and simulations of SO
In the MIPAS data of the tropics, where the tropopause height is relatively constant at around 16–17 km, a clear transition from elevated sulfur mole fractions in the troposphere to lower sulfur loading in the stratosphere is observed during times of weak volcanic influence (May–June 2009; Fig. 8c). The relatively high values at around 13–16 km in the measurements have already been noted in Fig. 5 and are supposed to be only partly connected to volcanic eruptions. A certain influence of elevated retrieved aerosol values due to cirrus clouds that have not been captured by the ice filter (Sect. 3.1) is possible. It is not clear to what extent the observed enhancements in the measurements (Fig. 8a–c) are caused by the eruptions of Kasatochi and Sarychev. In the case of Kasatochi, model simulations suggest that enhancements are confined primarily to altitudes above approximately 16 km. Additionally to the tropical enhancements at 13–16 km, the eruption of Dalaffilla in November 2008 overlays the observed sulfur that has been emitted by Kasatochi. The CTM simulations of Sarychev indicate that sulfur observed at altitudes as low as 12 km can be attributed to the volcanic eruption.
Time series of SO
Differences between the presented zonally averaged measurements and model
results arise partly from the fact that MIPAS measurements are not uniformly
distributed and data were filtered; they are also due to sparse data coverage in the
case of the CTM up to an altitude of 12–13 km. Data are partly missing in
relatively large areas, which may lead to biased zonal means. In the
measurements, data for SO
In Figs. 9 and 10, time series of latitudinally resolved mole fractions show
the transport of SO
As Fig. 9 but for sulfate aerosol.
Both in the measurements and simulations, most of the sulfur contained in
SO
In this study a new data set of MIPAS/Envisat global aerosol volume densities
and associated liquid-phase H
In a case study we investigate the evolution of volcanic sulfur after two
major midlatitude volcanic eruptions of the last decade (Kasatochi in 2008 at
51.2
Our findings of the residence time and transport pathways of enhanced sulfate aerosol in the midlatitude lower stratosphere have implications for the forcing of surface climate by moderate-sized midlatitude volcanoes and proposed geoengineering schemes. Sulfur injections into the lowermost stratosphere in midlatitudes can not only affect the extratropics of the respective hemisphere but are potentially transported towards the tropics, where they can undergo uplift and further transport by the Brewer–Dobson circulation and can thereby reach the other hemisphere.
The MIPAS data sets for aerosol volume densities and
liquid-phase H
To reduce biasing of zonal averages due to non-uniformly distributed data, we
use a method of increasing areas. It is based on the horizontal grid of our
chemical transport model, which has a resolution of
AG developed and performed the model simulations, wrote most of the paper and conducted most of the analyses. MH developed the MIPAS aerosol retrieval and provided the retrieval sensitivity studies and error estimations and their description (Sect. 3.1). BMS provided advice with the development and analysis of chemical transport modelling. TvC, GS, and MH provided advice for the analyses of MIPAS data. SG provided the flags for the MIPAS ice and ash filter. TD provided the balloon-borne in situ data and their description (Sect. 2.2). All authors contributed to the discussion of the results.
The authors declare that they have no conflict of interest. Thomas von Clarmann and Gabriele Stiller are ACP co-editors but have not been involved in the evaluation of this paper.
Parts of this work were supported by the European Commission's Seventh Framework Programme (FP7/2007–2013) within the StratoClim project (grant no. 603557), the National Science Foundation (award numbers 0437406 and 1011827), and the Helmholtz Association through the programme Atmosphere and Climate (ATMO). Meteorological analysis data by ECMWF and MIPAS level-1b calibrated spectra by ESA are acknowledged. The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. Edited by: Anja Schmidt Reviewed by: Hugh C. Pumphrey and one anonymous referee