ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-13161-2015Reassessment of MIPAS age of air trends and variabilityHaenelF. J.florian.haenel@kit.eduStillerG. P.https://orcid.org/0000-0003-2883-6873von ClarmannT.FunkeB.https://orcid.org/0000-0003-0462-4702EckertE.https://orcid.org/0000-0003-1517-5869GlatthorN.GrabowskiU.KellmannS.KieferM.LindenA.ReddmannT.https://orcid.org/0000-0003-1733-7016Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research, Karlsruhe, GermanyInstituto de Astrofísica de Andalucía (CSIC), 18008 Granada, SpainF. J. Haenel (florian.haenel@kit.edu)27November2015152213161131761April201526May201527October20159November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/13161/2015/acp-15-13161-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/13161/2015/acp-15-13161-2015.pdf
A new and improved setup of the SF6 retrieval together with a newly
calibrated version of MIPAS-ENVISAT level 1b spectra (version 5, ESA data
version 5.02/5.06) was used to obtain a new global SF6 data set, covering
the total observational period of MIPAS from July 2002 to April 2012 for the
first time. Monthly and zonally averaged SF6 profiles were converted into
mean age of air using a tropospheric SF6-reference curve. The
obtained data set of age of air was compared to airborne age of air
measurements. The temporal evolution of the mean age of air was then investigated
in 10∘ latitude and 1–2 km altitude bins. A regression
model consisting of a constant and a linear trend term, two proxies for the
quasi-biennial oscillation variation, sinusoidal terms for the seasonal and
semiannual variation and overtones was fitted to the age of air time series.
The annual cycle for particular regions in the stratosphere was investigated
and compared to other studies. The age of air trend over the total
MIPAS period consisting of the linear term was assessed and compared to
previous findings of . While the linear increase of
mean age is confirmed to be positive for the northern midlatitudes and
southern polar middle stratosphere, differences are found in the northern
polar upper stratosphere, where the mean age is now found to increase as
well. The magnitude of trends in the northern midlatitude middle
stratosphere is slightly lower compared to the previous version and the
trends fit remarkably well to the trend derived by .
Negative age of air trends found by are confirmed
for the lowermost tropical stratosphere and lowermost southern
midlatitudinal stratosphere. Differences to the previous data versions occur
in the middle tropical stratosphere around 25 km, where the trends
are now negative. Overall, the new latitude–altitude distribution of trends
appears to be less patchy and more coherent than the previous one. The new
data provide evidence of an accelerating shallow branch of the Brewer–Dobson
circulation, at least in the Southern Hemisphere. Finally the age of air
decadal trends are compared to trends calculated with simulated SF6
values by the Karlsruhe Simulation Model of the Middle Atmosphere (KASIMA)
and good agreement is found. The hemispheric asymmetry in the trends found in
the MIPAS data is also indicated in the trends calculated with simulated
SF6 values by the KASIMA model.
Introduction
While it is widely accepted that climate change with enhanced greenhouse-gas
abundances leads to a warming of the troposphere and a cooling of the
stratosphere, the secondary effects, in particular on the global circulation
in the stratosphere, the Brewer–Dobson circulation (BDC), are still an issue
of current research . A changing BDC will have large
impact on the overall composition of the stratosphere, on the ozone budget
and distribution and on the lifetimes of
ozone-depleting substances such as CFCs and
greenhouse gases. The mean age of air (AoA), which is the average transit time of
an air parcel from the entry point of the stratosphere, the tropical
tropopause, has become a measure for the strength of the BDC in particular
for observational analysis . The mean age of air
comprises both information on the speed of the advection and the amount of
mixing and stirring exerted on the air parcel. Modern general circulation
models and chemistry–climate models consistently simulate an
acceleration of the BDC in a greenhouse-gas-induced changing climate
.
So far, however, this expected speeding up of the BDC has not been confirmed
by observations. provided a 30-year record of
mean age of air derived from CO2 and SF6 balloon-borne
measurements which showed a slight but insignificant increase of mean age
over the years 1975–2005 for northern midlatitudes, which would indicate
a decelerated BDC. reported an acceleration of the
shallow branch of the BDC for the time period 1979–2009, while they found an
unchanged deep branch. investigated the age of stratospheric
air in the ERA-Interim reanalysis over the period 1989–2010 and stated that
the shallow and the deep branch of the BDC may evolve differently. They found
a negative and significant age of air trend in the lower stratosphere and
a positive but insignificant trend in the middle stratosphere.
provided the first global data set on
age of air derived from satellite SF6 measurements. In their paper
MIPAS-ENVISAT level 1b spectra of versions 3 and 4 were used to retrieve
vertical profiles of SF6 distributed over the whole globe for the
time period September 2002 to January 2010. Monthly zonal means were
converted into mean age of air, from which decadal trends were inferred for
latitude and altitude bins.
The derived age of air trends were found to be spatially inhomogeneous with
regions of increasing mean age of air and regions of decreasing age of air.
The non-homogeneity of trends was also reported by , who
also found a significant increasing trend in the mean age of air over
northern midlatitudes in an multiannual CTM simulation driven by ERA-Interim
winds over the period 1990–2009 and confirmed the measurements by
and . In their model study
they already noticed a hemispheric asymmetry, which was also later found by
with SLIMCAT model calculations. confirmed
this hemispheric asymmetry with calculations of the CLaMS model, also driven
by ERA-Interim data, and found positive trends in the Northern Hemisphere and
negative trends in the Southern Hemisphere for the time period 2002–2012.
The work presented here is a continuation of the work of
. An extended and improved SF6 data set is
provided on the basis of a newly calibrated version of MIPAS-ENVISAT level 1b
spectra (version 5, ESA data version 5.02/5.06). This new global
SF6 data set for the first time covers the total MIPAS period from
July 2002 to April 2012.
The characteristics of the MIPAS instrument are presented in
Sect. . The improvements on the retrieval setup are discussed
in Sect. . The characteristics and morphology of the
new global SF6 data set and the resulting age of air data set are
assessed in Sect. . Then the temporal development is
investigated and compared with the previous findings
(Sect. ). In Sect. , MIPAS-derived AoA
trends are compared with trends calculated with simulated SF6 values
from the Karlsruhe Simulation Model of the Middle Atmosphere (KASIMA).
Finally, in Sect. , we summarize the lessons learned about
possible changes of the BDC.
MIPAS
MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) is
a Fourier transform infrared spectrometer aboard ENVISAT
(Environmental Satellite; ) and was designed for the
detection of mid-infrared limb emission spectra in the middle and upper
atmosphere. The atmospheric spectra were inverted into vertical profiles of
atmospheric pressure, temperature and volume mixing ratios (vmrs) of at least
30 trace constituents. Details of the MIPAS instrument can be found in
.
In 2004 the operation of the MIPAS instrument was interrupted due to
a problem with the interferometer slide. The optical path difference was then
reduced, implying a deterioration of the spectral resolution from 0.025 to
0.0625 cm-1. The first phase of the mission (2002–2004) is
usually referred to as the MIPAS full-resolution (FR) period, while the
second phase (2005–2012) is called the reduced-resolution (RR) period.
Because of the long optical path through the atmospheric layers, MIPAS could
also detect trace gases with very low mixing ratios. Vertical information was
gained by scanning the atmosphere at different elevation angles with
different tangent altitudes. MIPAS could observe atmospheric parameters in
the altitude range from 5 to 160 km with minimum and maximum steps of
1 and 8 km respectively .
Improvement of the retrieval of SF6 mixing ratios
Data processing relies on constrained least squares fitting using the
Tikhonov regularization approach. Further details of the
MIPAS data processor used are described in
. Information on temperature
and line of sight, as well as the spectral shift, was taken from preceding
MIPAS retrievals performed prior to SF6 in the sequential retrieval
chain.
While the retrieval of SF6 by relied on ESA
version 4.61/4.62 and 4.67 calibrated radiance spectra, we have used version
5.02/5.06 spectra provided by ESA in the course of reprocessing of the data.
These data are considered superior with respect to 4.61/4.62/4.67, in
particular because the spectra of the FR period no longer suffered from a
calibration insufficiency which was reported as “baseline oscillations” in
and the whole data set is better calibrated now.
Further technical details on the MIPAS level 1b data can be found at
https://earth.esa.int/web/sppa/mission-performance/esa-missions/envisat/mipas/products-and-algorithms/products-information.
Contributing trace gases to a typical spectrum measured at
midlatitudes in July at 20 km (low resolution) with the SF6
signature in red.
Beyond this, the SF6 retrieval setup has been improved over that used
by . Improvements are related to the consideration
of non-local thermodynamic equilibrium emission of interfering CO2
lines, the treatment of interfering species in general and the details of
the joint retrieval of the background continuum. The implementation of the
altitude dependence of the regularization strength has been slightly changed.
The definition of the analysis window (941–952 cm-1, see
Fig. ), the regularization strength of the inverse
problem and the spectroscopic database chosen remained unchanged since no
improvements over the approach by could be achieved
with respect to these. Spectroscopic data were used from a dedicated MIPAS
database for gases like H2O, CO2, O3 and COF2
(). For N2O, NH3, CFC-12 and SF6
the spectroscopic database HITRAN2000 was used. Tests
with varying regularization parameters did not lead to any retrieval
improvements, i.e. the regularization strength chosen by
has been confirmed to be adequate. The new
SF6 data described and used here are version V5h_SF6_20 for the FR
data product and V5r_SF6_222 and V5r_SF6_223 for the RR period. The
latter two data versions have no discernible differences; their different
version numbers just reflect different sources of ECMWF meteorological
analysis data used in the retrieval. In the FR V5h_SF6_20 data version the
artefact of the previous version caused by radiance baseline oscillations in
the level 1 data described in is no longer an issue and
has been totally overcome.
Non-local thermodynamic equilibrium
The Q branch of the ν3 band of SF6 at 947.9 cm-1
analysed here is strongly superimposed by the CO2 laser band (00011
→ 10001) at 947.74 cm-1 and lies just above the first
hot band (01111 → 11101) line at 947.94 cm-1 (see
Fig. ). These CO2 emission bands deviate
from local thermodynamic equilibrium (LTE) in the middle atmosphere,
particularly during daytime. approximated the
non-LTE effect by treating the CO2 laser band and hot band emissions
as emissions from different (non-CO2) species and by fitting their
“virtual abundances” along with the SF6 retrieval. While these
virtual abundances have no physical meaning, they helped to model fairly well
the CO2 laser band emission and to avoid related spectral residuals
and error propagation. Contrary to that, our refined analysis relies on
explicit modelling of the non-LTE emissions of the CO2 laser and hot
bands. In the course of a preceding CO retrieval , the
vibrational temperatures of the CO2 laser band and the hot band were
calculated for the actual atmospheric conditions. Since the radiative
transfer code used in our retrieval, the Karlsruhe Optimized and Precise
Radiative Transfer Algorithm (KOPRA,
), supports
calculation of non-LTE emissions, these could directly be used for the
calculation of the laser band signal and hot band emissions. This improves
considerably the description of the CO2 emissions and reduces the
residuals between the observed and modelled spectra, leading eventually to
improved SF6 results (compare residuals at the position of
CO2 lines in Fig. ).
Coadded spectra (measured and modelled) and residuals for tangent
height 12 (approx. 24 km) over 1 day for the final retrieval setup
(upper panels) and for the previous retrieval setup (lower panels).
Interfering gases
Figure shows the spectral window used for the
SF6 retrieval and the expected spectral contributions of contributing
species for MIPAS reduced resolution at 20 km in midlatitudes for
July. The signature of the target species SF6 (red solid line) is
quite weak compared to some of the interfering species. Thus a careful
treatment of the interfering species is essential to minimize related error
propagation. Since for some of the interfering species no reliable a priori
information on their abundances is available, these gases are jointly fitted
along with SF6. For other interferents, abundance information is
available from preceding MIPAS retrievals; however, inconsistent spectroscopic
data or calibration inconsistencies in the SF6 analysis window and
the interferents' dedicated analysis windows can cause artefacts when the
known abundances are used to model the contribution of these gases in the
SF6 analysis window. Thus, it is occasionally adequate to jointly fit
these gases along with SF6, too. have used
abundance information from climatologies or preceding MIPAS retrievals for
all interfering species except for CO2 and H2O, which were
fitted jointly along with SF6. In the new retrieval scheme the trace
gases COF2 and ozone were additionally joint fitted. This helped to
minimize the residual of the fit and also removed a slight tilt of the
residual in spectral space. For all gases a first order Tikhonov-type
regularisation was chosen, which means that the slope of the profile was
forced to some constraint, rather then forcing the profile towards an
a priori profile as done in the optimal estimation approach. Usually the
constant zero profile served as a priori profile and in this way oscillations
in the profile are damped and the profile becomes smoother. In the following
we present a gas-by-gas discussion of our treatment of all interfering
species.
CO2
CO2 is the main contributing gas of all emitters in the SF6
analysis window (microwindow) used (blue solid lines in
Fig. ). As mentioned above the maximum of the
SF6 spectral signature is just underneath of the wing of
a CO2 laser line. In order to get an accurate value of the
SF6 mixing ratio from radiances emitted in the SF6
microwindow, a very precise modelling of the CO2 is crucial. We have
used a non-LTE model to account for the CO2 emissions in the middle
atmosphere and fitted it jointly with SF6. The first guess profile in
the iterative procedure was taken from climatologies .
H2O
A water vapour signature is located near the SF6 Q branch at
948.26 cm-1, so a considerable information crosstalk is expected
between the H2O and the SF6 signals. The water vapour profile
resulting from the preceding retrieval in dedicated microwindows (prefit) was
used as a priori and first guess profile for every geolocation. The
regularization strength associated with H2O was adjusted such that
the correction with respect to the initial H2O profile had about 1 to
1.5 degrees of freedom. Basically the profiles have the shape of the prefit
profile and only a shift of the prefit profile is allowed. The residual near
the water vapour line was not reduced when the regularization for water
vapour was relaxed. Variations of the regularization for water vapour or the
choice of related a priori (constant zero or water prefit) did not have any
discernible effect on the SF6 retrieval.
COF2
There is not much vertically resolved information contained on COF2
in the microwindow, but fitting this trace gas jointly with the target
species helped to minimize the residuals. A climatological profile served as
a priori and first guess profile since there was no prefit of COF2
available. The regularization was chosen relatively strong such that the
resulting COF2 profiles have about 1.5 to 3.5 degrees of freedom.
O3
Like COF2, ozone does not contribute much to the signal in the
microwindow, but prefits from the ozone retrieval existed. Thus, the ozone
prefits served as a priori and first guess profiles. Together with the joint
fit of COF2, the joint fit of ozone helped to remove a tilt in the
residual. The regularization applied allowed the ozone profiles to have about
1.5 to 3.5 degrees of freedom.
Further species
Profiles of N2O, NH3 and CFC-12 were imported from a
database . Since the signals of these gases are small,
related uncertainties are tolerable.
Background continuum and radiance offset
In the previous SF6 retrieval by background
continuum radiation was considered up to an altitude of 33 km. In the
atmosphere continuum radiation, i.e. radiation which is only varying very
slightly in spectral space (in contrast to spectral lines), is emitted by
clouds, dust or other aerosol particles. Also the sum of very far wings of
spectral lines, no more accounted for by the line by line calculation, can
contribute to the continuum radiation. In general it was assumed that
a consideration of continuum radiation above 33 km was not necessary,
particularly because there are no aerosol contributions expected above the
Junge layer. However, it turned out that fitting continuum radiation up to
higher altitudes (50 km) could eliminate an artefact in the retrieved
SF6 profile: while in the retrieval of an
unexplained local maximum in SF6 occurred around 36 km in the
tropics, this supposedly unphysical feature vanishes completely with the new
continuum treatment. This provides evidence that there is additional
continuum radiation in the atmosphere which if not accounted for leads to
elevated SF6 mixing ratios, since the SF6 signature is also
of broad band nature. The approach of a joint fit of the continuum radiation
up to higher altitudes also helped to improve retrievals of other species. In
addition, a recent paper pointed out that there is evidence of aerosol
particles even above the Junge layer due to meteoric dust
. In our retrievals we also fit a constant radiance offset
jointly in order to account for a possible residual shift in radiance due to
imperfect radiance calibration. This offset had to be strongly regularized in
order to cope with the pronounced linear interdependence of the continuum and
offset Jacobians.
Miscellaneous
The consideration of continuum up to higher altitudes allowed the usage of
more upper tangent heights. While the previous retrieval setup only used the
first 19 out of 27 tangent heights in MIPAS reduced-resolution mode, the new
setup incorporated information from measurements of 22 tangent heights. This
allowed more information from higher altitudes to be gained, i.e. the
averaging kernel diagonals increased slightly at higher altitudes. In
addition, the root mean square (RMS) of the residuals in upper tangent heights
decreased. Hence, with the new retrieval setup for the first time it made
sense to include 22 tangents height instead of 19. The new mean SF6
profiles contain more information in the altitude range 40–50 km,
show more structure and depend less on the prior information there.
Discussion of the retrieval refinement
In Fig. an example for the residual between measured
and simulated spectrum at tangent height 12 (approx. 24 km) of the
final retrieval setup (upper panels) and the previous setup (lower panels) is
shown. To reduce the noise, measured and modelled spectra have been coadded
over the period of 1 day. One can see that the residuals improved
substantially. Especially the CO2 lines and the water vapour line
(compare with Fig. ) are fit much better and overall
the RMS of the residual has been reduced from about 1.8 to
1.0 nW(cm2srcm-1)-1 in this example.
Previous and final RMS of the residual for tangent altitude 12, 14 and 16 with coadded spectra over 1 day.
Also a slight tilt of the residual in spectral space was removed. The
improvements achieved in the residuals are dependent on altitude and
latitude. In Table we present previous and final RMS of the
residuals between measured and modelled spectra resolved in latitude bands of
30∘ for our example day for three selected tangent altitudes. The
relative improvements are largest in the tropics, amounting to about 40 %,
and are smallest in the northern polar stratosphere, where infrared radiances
are small, because our example data were a day in boreal winter.
With the new retrieval setup the unexplained “nose”, a local maximum in the
tropical SF6 profiles at 36 km, no longer appeared after
considering the continuum above the standard altitude of 33 km up to
an altitude of 50 km.
The vertical resolution of the new SF6 data is slightly degraded
compared to the previous version and varies now from 4 to 6 km at
20 km, from 7 to 10 km at 30 km and from 12 to
18 km at 40 km altitude due to the inclusion of more gases
in the fit, which was done to achieve a higher accuracy and less systematic
errors.
The new SF6 data set and age of air distributions
With the new retrieval setup, the complete set of nominal mode MIPAS data was
processed and approx. 2.3 million SF6 profiles have been retrieved.
The profiles belong to geolocations that cover the whole globe and the full
MIPAS period from July 2002 to April 2012 but with several data gaps in
between. The single profiles scatter a lot and the noise error is too large
(in the order of 20 %) to provide useful age of air information from
single profiles. However, averaged profiles lead to meaningful
SF6 profiles.
The new SF6 data set exhibits similar features as the previous one
described in , e.g. the SF6 vmrs are
increasing with time at all latitudes. Seasonal influences can be identified,
such as very low mixing ratios at the end of austral winter in the southern
polar latitudes. A figure showing the time series of SF6 over
latitude is included in the Supplement.
Conversion of SF6 into age of air
For the calculation of AoA from SF6 abundances
a SF6 reference curve is necessary. The theoretical concept of age of
air as derived by requires the knowledge of SF6 mixing
ratios at the entry point into the stratosphere, i.e. the tropical tropopause
region, over a long period of time. As pointed out by
such a long-term observational data set is not
available. Only ground-based observations can provide the necessary reference
data. However, transport times from the surface to the tropical tropopause
are somewhat uncertain and can amount to days or even hours (to the top of
convection) to weeks or months (to the top of the tropical tropopause layer). Using surface data as a reference can imply a high bias in this order
of magnitude on the AoA data.
This has to be kept in mind when comparing MIPAS AoA distributions to model
data, for which time 0 is set by tropopause crossing of the air parcel.
We have constructed the SF6 reference curve as described in
using NOAA/ESRL SF6 data. For the period
1995 to November 2013 smoothed ground-based global-mean combined flask and
in situ data are used, while for times before 1995
a linear approximation from (y=0.125+0.215×(t-1985)) is applied. The reference curve is extended with a linear
extrapolation until June 2014 to deal with MIPAS SF6 values slightly
higher than the reference values at that certain time that can occur
sporadically due to their random errors.
Zonal mean distribution of mean age of stratospheric air for the
four seasons, derived by averaging MIPAS AoA data of all available years for
the respective season.
Differences of zonal seasonal mean distribution of mean age of
stratospheric air to the previous data version averaged for the four
seasons.
The AoA is then calculated by simply mapping the measured SF6 value
on the reference curve and reading of the reference time. The time
difference, the so-called lag time, approximates the AoA. According to
this lag time is only equivalent to the mean age of air when
the used tracer is growing strictly linear, i.e. the reference curve has to
be linear. Because our constructed reference curve appears to be slightly
non-linear, a correction is applied. Within an iterative procedure the
reference curve is convoluted with a typical age spectrum. More details of
this non-linearity correction are discussed in .
SF6 is a stable tracer in the stratosphere. However, it has
a mesospheric sink. Every winter SF6-depleted air from the mesosphere
subsides into the polar vortex leading to “apparent ages” which are
considerably larger than the true ages. This “overaging” is most pronounced
in the polar vortices, where AoA derived from SF6 can be greater by 2
or more years . However, due to in-mixing of some of the vortex
air into midlatitudes, the entire stratosphere is affected to a certain
degree. This should be kept in mind when comparing AoA calculated from
SF6 abundances with AoA calculated from other tracers or model
studies. estimated the global effect of overaging
to about 0.08 years per year of age for the Southern Hemisphere and
to about 0.04 years per year of age for the Northern Hemisphere.
Global distribution of AoA
The derived monthly zonal means of AoA have a precision in terms of the
standard error of the means of 0.06–0.4 years for the reduced-resolution period and of 0.08–0.5 years for the full-resolution
period. Most of the monthly means are composed of 500–800 single values when
fully occupied.
The global distribution of the newly derived AoA data set can be seen as
average over all years for the four seasons in Fig. .
Highest AoA values occur in the polar stratosphere in hemispheric winter to
spring being particularly high in the Southern Hemisphere. This again can be
explained by intrusion and subsidence of old upper stratospheric and
mesospheric air into the polar vortex. Due to the mesospheric SF6
sink, this mesospheric air appears even older than it actually is.
The differences in the zonal monthly means of AoA to the previous data set,
averaged over all years for the four seasons, are presented in
Fig. . The main difference to the old data set
is that the local minimum of AoA in the tropics around 36 km is no
longer present in the new data set. This feature of the old data set has been
proven to be a retrieval artefact, which was eliminated by a refined
treatment of continuum radiation (see Sect. ). This
artefact triggered an oscillation in lower layers which are no longer present
in the new data set. Above 40 km, the air is now found to be younger
at almost all latitudes, which appears to be more realistic. The old data
version was reported to have a possible high bias of up to 2 years
above 35 km, most pronounced at the summer pole due to the simplified
approach concerning the non-LTE treatment of interfering CO2 lines
. The full non-LTE treatment used for the new data set
has removed this systematic uncertainty. In addition, part of the lower AoA
in the upper stratosphere is attributed to the revised regularization of the
retrieval.
Among studies of AoA (e.g.
) it became a standard for
validation of measured or modelled AoA to compare with earlier airborne
measurements from the 1990s as published by and
. Overall this comparison on a latitudinal cross-section at
20 km turns out to be quite similar to the one in Fig. 4 in
: the agreement of MIPAS AoA with the earlier
airborne measurements is excellent in the northern and southern
midlatitudes, whereas in the tropics and high latitudes MIPAS exhibits
higher age. There are only small differences to the comparison with the
previous MIPAS AoA version, like the spread of MIPAS AoA in the tropics is
lower and the negative peak of low MIPAS AoA at about 30∘ N is no
longer present in the new version. The respective figure is attached in the
Supplement.
Comparison of MIPAS AoA profiles with airborne profiles of the 1990s
for the tropics (5∘ S), the northern midlatitudes (40∘ N)
and the northern high latitudes (65∘ N).
In Fig. MIPAS AoA profiles are compared to airborne
AoA profiles (in situ CO2 measurements by
; in situ SF6 measurements by
; air sample measurements by ) for
the tropics (5∘ S), the northern midlatitudes (40∘ N) and
the northern high latitudes (65∘ N). In the tropics MIPAS AoA is
older than in situ CO2 and SF6 measurements at all altitudes.
In the northern midlatitudes the MIPAS profile fits excellently to the
SF6 in situ data up to an altitude of 27 km and is older
higher up. As expected, in situ CO2 measurements provide lower ages,
and the AoA from SF6 air samples by is
younger, too. At northern high latitudes, MIPAS age profiles only fit well to
the SF6 air samples taken from polar vortex air. To illustrate the
high seasonality, monthly averaged MIPAS profiles are additionally shown with
oldest ages found for January.
2.45/4.5
Observed temporal variability for the period July 2002 to April 2012
For the analysis of the temporal variability of the new AoA data set the same
methods were applied as in , i.e. the following
regression function was fitted to the data:
age(t)=a+bt+c1qbo1(t)+d1qbo2(t)+∑n=29cnsin2πtln+dncos2πtln
where t is time, qbo1 and qbo2 are the
quasi-biennial oscillation (QBO) indices and the sum represents eight sine and
eight
cosine functions of the period length ln. The period of the first two sine
and cosine functions is 12 and 6 months respectively, representing the
seasonal and the semiannual cycle. The other six terms have period lengths of
3, 4, 8, 9, 18 and 24 months and describe deviations of the temporal
variation from a pure sine or cosine wave. Fitting sine and cosine of the
same period length accounts for a possible phase shift of the oscillation.
The terms qbo1 and qbo2 are the normalized Singapore
winds at 30 and 50 hPa as provided by the Free University of Berlin
via http://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/index.html.
These QBO proxies are approximately orthogonal such that their combination
can emulate any QBO phase shift . For the fit of the
coefficients a, b, c1, …, c9, d1, …, d9 to the
data, the method of is used, which considers the
full error covariance matrix of mean age data Sm with
the squared standard errors of the means of the monthly zonal means as
the diagonal terms .
An example of the fit of our regression model to MIPAS monthly zonal mean
data can be seen in the Supplement. The fit considers a potential
bias of the two MIPAS measurement periods (dashed orange line) as described
in . Such a fit is done for every
10∘/1–2 km latitude–altitude bin.
Age of air trends
In the time series analysis was first discussed
within the framework of descriptive statistics, i.e. without consideration of
the autocorrelations in the residuals of the trend analysis. As a second
step, the analysis was repeated within the framework of inductive statistics,
where autocorrelated model errors have to be considered. In this study we
focus on the trend analysis which is referred to as “model-error corrected
linear increase” in , because the analysis without
consideration of the autocorrelated model errors leads to very similar trends
whose significances, however, are considerably overestimated.
As described in our regression model only accounts
for the linear trend, several periodics and the QBO terms. Other atmospheric
variability, especially from non-periodic events, is not included in this
model. This results in fit residuals which are considerably larger than the
data errors represented by the covariance matrix Sm,
which includes only the standard errors of the monthly means and the
correlated terms to account for the possible bias between the MIPAS data
subsets . Therefore the
χreduced2 of the fit with
χreduced2=(ageMIPAS-agemodelled)TSm-1(ageMIPAS-agemodelled)m-n
exceeds the value of unity in most cases, where ageMIPAS
and agemodelled are the data vectors containing the
measured and modelled age values respectively and where m and n are the
number of data pairs and the number of fitted coefficients respectively
. In order to consider the model errors of the
regression model, the autocorrelation of two adjacent data points was
estimated in a first step. In a second step the fit was rerun with the
autocorrelation and a constant error term added to the covariance matrices.
These additional terms in the covariance matrices were scaled within an
iterative procedure, such that the resulting χreduced2 of
the trend fit was close to unity. This iterative procedure is necessary
because the additional autocorrelated error term changes the weight between
the data points in the fit.
The linear increase of AoA over the whole MIPAS period derived from our
regression analysis varies with altitude and latitude. The global view can be
seen in Fig. top panel. Red areas indicate increasing AoA,
while blue regions indicate decreasing AoA. Hatched areas indicate where the
trend is not significant, i.e. it is smaller in absolute terms than its
2σ uncertainty.
The overall pattern of linear increase/decrease is in good agreement with the
respective picture of the trend fit without consideration of autocorrelation
and empirical errors (see respective figure in the Supplement), which
confirms that our method is robust. The significance of most data bins is
lower, as expected, due to the additional error terms.
Top: Altitude–latitude cross-section of the model-error corrected
linear increase of MIPAS AoA over the years 2002 to 2012, i.e. after
including the model error and autocorrelations between the data points in the
fit. Hatched areas indicate where the trend is not significant, i.e. it is
smaller (in absolute terms) than its 2σ uncertainty. Bottom:
1σ uncertainty of the trend in terms of years/decade.
The distribution of trends in the latitude–altitude plane roughly confirms
the mean trends of those obtained by and their
general morphology but looks more coherent and less patchy, meaning that
regions of linear increase and decrease respectively are more contiguous.
There are basically two regions of linear decrease: a large one consisting of
the tropics and southern subtropics between about 19 to 30 km and
extending to the lowermost midlatitudinal southern stratosphere and
a smaller one consisting of the upper tropical troposphere extending to the
lowermost stratosphere (LMS) of midlatitudes. These regions are surrounded by
regions of AoA linear increase. Largest positive linear trends were observed
in the polar regions. Compared to findings of
a positive linear increase of mean age is confirmed for the northern
midlatitudes and southern upper polar stratosphere. Negative age of air
trends of in the lowermost tropical stratosphere
and lower southern midlatitudinal stratosphere are also confirmed.
Differences are found in the northern polar stratosphere, where the mean age
is now increasing as well. In the tropical stratosphere the picture is now
almost opposite to the previous data of , meaning
that AoA is increasing where it used to be decreasing and vice versa. These
changes are attributed to the more adequate treatment of the background
continuum emission in the retrieval and the associated removal of the
spurious SF6 maximum and subsequent errors. A clear asymmetry between
the hemispheres is visible.
Vertical profiles of the age of air linear increase/decrease over
the years 2002 to 2012 for example latitudes. Horizontal bars give the
2σ uncertainties of the linear variations. The 30-year trend as derived
by for the northern midlatitudes is also shown for
comparison as a black cross indicating its valid altitude range and its
2σ uncertainty.
The uncertainties (see Fig. , bottom panel) are now more
realistic, since now an additional model error has been added; however, they
are smaller than the ones derived by , which is
attributed to the longer time series, covering now the full MIPAS period, and
the fact that the new AoA data set is less noisy than the previous one.
The vertical profiles of AoA linear trends for every other latitude bin are
shown in Fig. , top panel.
derived a trend of AoA for 30 to 50∘ N of +0.24 ± 0.22 year per
decade (1σ uncertainty level) for the 24 to 35 km altitude
range for 1975–2005. This trend together with its valid altitude range and
its 2σ uncertainty is marked as big black cross in
Fig. . For better illustration the same picture with
the MIPAS linear trend profiles for the two relevant latitude bins is shown
in Fig. , bottom panel. The MIPAS AoA trends of 30 to
40 and 40 to 50∘ N are slightly lower than in the previous version
and match now impressively well with the trend estimated by
in the 24 to 35 km altitude region. One has to
keep in mind that the trend derived by represents the
time period 1975–2005, while MIPAS measured from 2002 to 2012. So there is
only a small time overlap between the two trends. Still the agreement of both
is remarkable. The MIPAS AoA trends for the latitude bins 30 to 40 and 40 to
50∘ N are significantly distinct from 0 for all altitudes above
22 km even on the 2σ uncertainty level.
Altitude–latitude cross-sections of amplitudes (top) and month of
the minimum (middle) and maximum (bottom) of the seasonal variation of the mean
age of air.
Annual cycle and QBO influence
Figure shows the amplitudes and phases of the seasonal
cycle, i.e. the amplitudes and phases of terms with period length
1 year determined with the regression model described above. Compared
to Fig. 9 in there are no substantial differences
in the new data set. Thus, their respective conclusions remain valid in the
light of the new data. Here we want to highlight the few differences and
continue with the discussion, in particular by comparing the results with
findings of other studies.
found polar stratospheric AoA above 25 km, with
youngest air at the end of local winter to spring, to be in the opposite
phase than in the lowermost extratropical stratosphere in their analysis of
ERA-Interim data. In the model analysis of the maximum of
AoA in the polar region in spring is also bounded to the lower stratosphere,
whereas the upper polar stratosphere exhibits younger age. In contrast the oldest
air in northern polar regions is found in MIPAS data in spring in the lower
stratosphere and in mid-winter in the higher stratosphere. This difference to
MIPAS AoA can be explained by the different derivations of AoA in the
respective studies: while in AoA is explicitly calculated by
backward trajectories of the air parcel, and in the AoA is
determined by the pulse tracer method, the MIPAS AoA is derived by
SF6 observations which exhibit an overaging when SF6-depleted
mesospheric air subsides into the polar stratosphere during winter. This
overaging in the polar stratosphere during winter shifts the phase in the
MIPAS data towards oldest air in polar midwinter, when subsidence of
mesospheric air is strongest.
Some discernible difference to the previous data set is that the band of high
seasonal amplitudes in the northern midlatitudes is not visible anymore in
the new distribution of amplitudes (Fig. , top panel).
Instead there is a region in northern midlatitudes above 25 km,
which also exhibits high amplitudes like the equivalent region in the
Southern Hemisphere. A higher amplitude of the seasonal cycle is now also
found in the extratropical southern LMS. Hence, now
both hemispheres show enhanced seasonal amplitudes in the extratropical LMS,
which are tentatively attributed to the seasonality of the permeability of
the subtropical jet and flooding of this region
with old vortex air after the vortex breakdown at the end of winter and
spring.
Consistently found high amplitudes of the seasonal cycle in
the southern and northern extratropical LMS. Most parts of both the southern
and northern extratropical LMS reach their maximum in AoA at the end of
local winter to spring in the MIPAS data set as well as in the analysis by
. This hemispheric symmetry is a feature of the new MIPAS
data set. found oldest AoA in the northern LMS in April
and youngest in October with in situ measurements of SF6 and
CO2 during the SPURT aircraft campaigns. MIPAS observed youngest air
in hemispheric late summer to autumn when the mixing barrier in the
subtropics is weakest and young air from the tropics is injected in this
region, also referred to as “flushing” of the LMS . Also
cross-tropopause isentropic mixing from the tropical troposphere in the
extratropical LMS is enhanced during summer–early autumn when the
subtropical jet is weak .
Model results of of the seasonal variation of AoA also agree
with MIPAS in the extratropical LMS.
In the northern subtropical lower stratosphere an abrupt meridional phase
shift of almost half a year occurs, which means that these air masses are
well isolated by the subtropical jet. Equatorwards the air is oldest in
summer, when the subtropical mixing barrier and the BDC are weakest and older
air from the extratropics is mixed in. This process is also indicated in the
Southern Hemisphere and these opposite phases between the subtropics and the
extratropics are also observed in the model results of .
In the midlatitudinal middle and upper stratosphere the air is youngest in
local winter, when, according to the known seasonality of the
Brewer–Dobson circulation, younger air is brought to higher latitudes more
efficiently. The mixing barriers are partially visible by abrupt phase shifts
in the month of minimum and maximum age respectively: air masses in the
polar vortex are well isolated from the rest of the hemisphere. The
subtropical mixing barrier is visible in the northern lower stratosphere at
30∘ N and is indicated in the upper stratosphere only in the
plot of maximum age of Fig. . In the Southern Hemisphere
the abrupt phase shifts seem to occur at 50–60∘ S and at
10–20∘ S.
In the tropics below approx. 28 km air is youngest in boreal winter,
even in the Southern Hemisphere (except for altitude–latitude bins below
20 km). The hemispheric difference is lower than expected, which was
also noticed by . However, this minimum in AoA in
the southern tropics occurs approx. 2 months later in the new MIPAS data. In
the Northern Hemisphere air is oldest in late summer, while it is oldest in
austral spring to early summer in the Southern Hemisphere. Furthermore this
maximum in AoA in the southern tropics occurs 2 months earlier in the new
MIPAS data set compared to the previous one.
The amplitude of the QBO signal in AoA is shown in Fig. for all
latitudes and altitudes under assessment. High amplitudes are found not only
in the tropics but also in the upper stratosphere at midlatitudes, whereas
highest amplitudes were found in the upper polar stratosphere. We also find
high amplitudes in the northern lowermost stratosphere.
Comparison with model simulation
The MIPAS SF6-based AoA trends for 2002–2012 are compared with trends
derived from SF6 distributions calculated with the KASIMA (see
for a description of the
model and some applications). Here we used the model in the configuration as
described in but with a T42/L63 configuration
corresponding to about 2.84∘×2.84∘ horizontal resolution and 63 vertical levels between 7
and 120 km. In addition, the model is nudged to ERA-Interim analyses
below 1 hPa. SF6 mixing ratio values were set at the lower
boundary of the model in the troposphere using NOAA/ESRL data. Note that the
model includes the mesospheric loss of SF6, which is implemented in
the model according to . Previously,
showed that the apparent high mean age values in late polar stratospheric
winter observed in MIPAS observations can only be reproduced by the model
simulations when including mesospheric loss.
Altitude–latitude cross-sections of amplitudes of the QBO variation
of mean age of air.
For the determination of the trend of SF6-derived mean age of air in
the model calculation, the SF6 distributions were calculated on
a pressure–latitude grid using 64 latitude bins. In each pressure–latitude
bin monthly zonal averages of SF6 were calculated together with their
standard error of the mean. The vertical pressure coordinates have been
converted to geometrical altitudes assuming an isothermal atmosphere with
a scale height H of 7 km (z=-Hln(p/p0)).
Afterwards the monthly zonal means have been interpolated on the MIPAS
altitude grid and binned in the MIPAS latitude bins. These regridded zonal
SF6 monthly means were sampled and converted to AoA in the same
manner as it was done for the measured SF6 values (see
Sect. ).
Figure shows the distribution of age trends calculated
with simulated SF6 values from the KASIMA model in
a latitude–altitude plane with consideration of empirical errors and
autocorrelations.
Calculated AoA trends for 2002–2012 from the KASIMA model with
consideration of empirical errors and autocorrelation. Hatched areas indicate
where the trend is not significant.
The model results agree remarkably well with the empirical AoA trends:
positive decadal trends are found in the upper polar stratosphere in both
hemispheres and at northern midlatitudes around 20 to 25 km while
negative trends are found in the tropics and southern subtropics as well as
in the southern lower and lowermost stratosphere at midlatitudes and
southern polar region. The most pronounced negative trend is detected in the
southern tropics and subtropics around 25 to 30 km, whereas it is
found around 25 km in the MIPAS measurements. At northern
midlatitudes at about 25 to 30 km altitude a tongue of negative
trend is modelled. While MIPAS detected still positive trends there, there is
at least a local age trend minimum in this region in the MIPAS data. At first
glance there seems to be no disagreement between MIPAS and KASIMA in this
region, because negative trends in KASIMA are not significant there. However,
one has to be careful with the significances when comparing MIPAS and KASIMA:
KASIMA is a nudged model, i.e. in wide parts of the atmosphere it represents
the real atmosphere. This implies that the atmospheric variability patterns
of KASIMA and MIPAS which are responsible for the error of the multilinear
model share certain components and therefore cannot be assumed as fully
uncorrelated. Thus, this error characterizes the expected difference between
the regression function and truth; however, it cannot necessarily account for
the differences between MIPAS and KASIMA. For this comparison the trends
without consideration of the model errors may be more adequate. These figures
are attached in the Supplement and show that the region of the “negative
tongue” is significant in KASIMA, whereas it is significantly positive in
MIPAS.
At high latitudes lower stratospheric trends are positive in the MIPAS but
negative in the KASIMA data set. These trends are not significant for MIPAS;
however, they are significant when comparing the respective figures of trends
without consideration of the model error (see figures in the Supplement). So
there is indeed a contradiction in this region, which is most likely due to
the “overaging” effect, which is more pronounced in the measured data
because KASIMA underestimates polar winter subsidence.
What is striking in Fig. is the hemispheric asymmetry
between significant negative trends in the Southern Hemisphere and
significant positive trends in the Northern Hemisphere, which was also found
in the MIPAS data. This hemispheric asymmetry was also noticed by
with the TOMCAT model and by with the
SLIMCAT model and was later also confirmed by with the
CLaMS model.
Summary and conclusions
In this work the SF6 retrieval setup for MIPAS ENVISAT spectra has
been improved over the one developed by and a newer
version of spectra provided by ESA (level 1b data, version 5.02/5.06) was
used to retrieve global profiles of the trace gas SF6. Monthly zonal
means were converted in AoA using a tropospheric reference curve. The new AoA
data set resembles roughly that of but shows
differences with respect to several details. Some spurious features of the
old data set no longer appear in the new data set. In particular, the new
data set does not show the local AoA minimum at 36 km in the tropics,
which was believed to be a retrieval artefact of the previous version and
could be eliminated by a refined consideration of continuum radiation.
A possible high bias of the old AoA data set above 40 km is removed
as the air is considerably younger in this altitude region in the new data
version.
The latitudinal cross-section of AoA at 20 km was compared to
airborne observations from the 1990s and no substantial differences to the
previous version of were found. The comparison of
AoA profiles with airborne measurements shows that in the tropics MIPAS AoA
is older at all altitudes. At northern midlatitudes MIPAS agrees with most
of SF6 in situ data whereas at high northern latitudes MIPAS is again
older, and only the SF6 air samples inside the polar vortex match with
the MIPAS data.
The temporal variability of AoA over the 10 years of MIPAS
measurements (2002–2012) was analysed by fitting a regression model to the
AoA time series. The annual cycle in AoA of particular regions in the
stratosphere was investigated and found to be in good agreement with other
studies.
The derived AoA decadal trends show a pronounced hemispheric asymmetry above
the lowermost stratosphere. The results of were
confirmed with respect to the typical values and the general morphology. The
overall picture of linear increase/decrease in the latitude–altitude plane,
however, is more contiguous and less patchy with the new data. Positive
linear trends were confirmed for the northern midlatitudes and southern
polar middle stratosphere whereas negative trends were confirmed for the
lowermost tropical stratosphere and lowermost southern midlatitudinal
stratosphere. Differences to the previous data set occur in the northern
polar upper stratosphere, where trends are now positive, and in the middle
tropical stratosphere, where trends are now negative. The latter might be
explained by the removal of the retrieval artefact which changed the shape of
the AoA profile in the tropics considerably. The linear increase in the
southern and northern polar stratosphere and in the northern midlatitudes
can be considered as robust results. The significant positive trend in the
northern midlatitudes supports the findings of and
the inferred trends match impressively well with the estimated trend by
.
The refined MIPAS observations on AoA in this study do not corroborate the
results of various model studies, which consistently predict a decreasing AoA
for the whole stratosphere. However, our decadal trends cannot be compared to
results from long-term model studies. Our comparison with the KASIMA model
for the period 2002–2012 shows that the linear increase in the upper polar
stratosphere and in the northern midlatitudes can be reproduced in the model
at least when data are sampled and analysed in the same manner as the MIPAS
data. It also demonstrates that the ERA-Interim data, used to nudge KASIMA,
apparently are able to reproduce the observed transport trend, which shows
that they are suitable for studies of the BDC and its trends.
Nevertheless this study finds a decreasing AoA trend in the tropics and in
the lower and lowermost midlatitudinal southern stratosphere in agreement
with long-term model studies and hence supports the idea of an increasing
shallow branch of the BDC, which was also proposed by
and supported by , at least in the Southern Hemisphere.
The Supplement related to this article is available online at doi:10.5194/acp-15-13161-2015-supplement.
Acknowledgements
This work was funded by the “CAWSES” priority programme of the German
Research Foundation (DFG) under project STI 210/5-3 and by the German Federal
Ministry of Education and Research (BMBF) within the “ROMIC” programme
under project 01LG1221B.
E. Eckert was funded by the DFG project CL 319/2-1 (COLIBRI). We would like
to acknowledge provision of MIPAS level 1b data by ESA and the SF6
data from the NOAA/ESRL halocarbons in situ programme. The authors also
acknowledge support by the Deutsche Forschungsgemeinschaft and Open Access
Publishing Fund of Karlsruhe Institute of Technology.
The authors like to thank the reviewers for their constructive comments.
The
article processing charges for this open-access publication
were covered by a Research Centre of the Helmholtz Association.
Edited by: M. Dameris
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