ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-10143-2017CCl4 distribution derived from MIPAS ESA v7 data: intercomparisons, trend, and lifetime estimationValeriMassimohttps://orcid.org/0000-0001-5041-9000BarbaraFlavioBooneChrisCeccheriniSimonehttps://orcid.org/0000-0002-8902-2040GaiMarcoMaucherGuidoRaspolliniPierahttps://orcid.org/0000-0002-5408-1809RidolfiMarcomarco.ridolfi@unibo.ithttps://orcid.org/0000-0002-3492-6472SgheriLucahttps://orcid.org/0000-0002-6014-9363WetzelGeraldZoppettiNicolahttps://orcid.org/0000-0002-7857-8895Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, ItalyIstituto di Scienze dell'Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, Bologna, ItalyIstituto di Fisica Applicata “Nello Carrara”, Consiglio Nazionale delle Ricerche, Florence, ItalyDepartment of Chemistry, University of Waterloo, Waterloo, Ontario, CanadaIstituto per le Applicazioni del Calcolo, Consiglio Nazionale delle Ricerche, Florence, ItalyKarlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Karlsruhe, GermanyMarco Ridolfi (marco.ridolfi@unibo.it)30August20171716101431016223December20162March201727June201718July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/10143/2017/acp-17-10143-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/10143/2017/acp-17-10143-2017.pdf
Atmospheric emissions of carbon tetrachloride (CCl4) are
regulated by the Montreal Protocol due to its role as a strong
ozone-depleting substance. The molecule has been the subject of recent
increased interest as a consequence of the so-called “mystery of
CCl4”, the discrepancy between atmospheric observations and reported
production and consumption. Surface measurements of CCl4 atmospheric
concentrations have declined at a rate almost 3 times lower than its
lifetime-limited rate, suggesting persistent atmospheric emissions despite
the ban. In this paper, we study CCl4 vertical and zonal
distributions in the upper troposphere and lower stratosphere (including the
photolytic loss region, 70–20 hPa), its trend, and its stratospheric lifetime
using measurements from the Michelson Interferometer for Passive Atmospheric
Sounding (MIPAS), which operated onboard the ENVISAT satellite from 2002 to
2012. Specifically, we use the MIPAS data product generated with Version 7 of
the Level 2 algorithm operated by the European Space Agency.
The CCl4 zonal means show features typical of long-lived species of
anthropogenic origin that are destroyed primarily in the stratosphere, with
larger quantities in the troposphere and a monotonic decrease with increasing
altitude in the stratosphere. MIPAS CCl4 measurements have been
compared with independent measurements from other satellite and balloon-borne
remote sounders, showing a good agreement between the different datasets.
CCl4 trends are calculated as a function of both latitude and
altitude. Negative trends of about -10 to -15 pptv decade-1
(-10 to -30 % decade-1) are found at all latitudes in the upper troposphere–lower stratosphere region, apart from a region in the southern midlatitudes
between 50 and 10 hPa where the trend is positive with values around
5–10 pptv decade-1 (15–20 % decade-1). At the lowest altitudes sounded by
MIPAS, we find trends consistent with those determined on the basis of
long-term ground-based measurements (-10 to -13 pptv decade-1). For higher
altitudes, the trend shows a pronounced asymmetry between the Northern and
Southern hemispheres, and the magnitude of the decline rate increases with
altitude. We use a simplified model assuming tracer–tracer linear
correlations to determine CCl4 lifetime in the lower stratosphere.
The calculation provides a global average lifetime of 47 (39–61) years,
considering CFC-11 as the reference tracer. This value is consistent with the
most recent literature result of 44 (36–58) years.
Introduction
Carbon tetrachloride (CCl4) is a strong ozone-depleting
substance with an ozone depletion potential of 0.72. It is also a strong greenhouse
gas with a 100-year global warming potential of 1730.
Regulated by the Montreal Protocol, the production of CCl4 for
dispersive applications was banned for developed countries in 1996, while
developing countries were allowed a delayed reduction with the complete
elimination by 2010 . CCl4 can still be legally used
as a feedstock, for example in the production of hydrofluorocarbons.
CCl4 natural emissions are not completely understood, which yields
some uncertainty on the magnitude of their contributions.
Stratosphere–troposphere
Processes and their Role in Climate (SPARC) has
recently defined an upper limit of the natural emissions (based on the
analysis of old air in firn snow) of 3–4 Gg yr-1 out of a total
emission estimation of 40 (25–55) Gg yr-1.
The dominant loss mechanism for atmospheric CCl4 is through
photolysis in the stratosphere. The other major sinks are degradation in the
oceans and degradation in soil. The estimated partial lifetimes provided in
the latest ozone assessment report with respect to these
three sinks are 44 years for the atmospheric sink, 94 years for the
oceanic sink, and 195 years for the soil sink. The combination of these
three partial loss rates yields a total lifetime estimate of 26 years.
CCl4 atmospheric concentration is routinely monitored by global
networks such as Advanced Global Atmospheric Gases Experiment (AGAGE;
http://agage.mit.edu/; ) and National
Oceanic and Atmospheric Administration/Earth System Research Laboratory/Halocarbons & other Atmospheric Trace Species
(NOAA/ESRL/HATS;
http://www.esrl.noaa.gov/gmd/hats/). The concentration of CCl4
has been decreasing in the atmosphere since the early 1990s, and the latest
ozone assessment report indicates that the global surface
mean mole fraction of CCl4 continued to decline from 2008 to 2012.
AGAGE and the University of California, Irvine (UCI) networks report rates of
decline of 1.2–1.3 % yr-1 from 2011 to 2012, whereas the rate
of decline reported by the NOAA/HATS network was 1.6 % yr-1.
These relative declines in mole fractions at the Earth's surface are
comparable to declines in column abundances of 1.1–1.2 % yr-1.
A significant discrepancy is observed between global emissions estimates of
CCl4 derived by reported production and feedstock usage (bottom-up
emissions) compared to those derived by atmospheric observations (top-down
emissions). This discrepancy has recently stimulated a particular interest in
furthering the understanding of atmospheric CCl4. A study performed
with a 3-D chemistry–climate model using the observed global trend and the
observed interhemispheric gradient (IHG; 1.5±0.2 ppt for 2000–2012)
estimated a total lifetime of 35 years . Recently, a study has
reassessed the partial lifetime with respect to the soil sink to be 375 years
, and another study has reassessed the partial lifetime with
respect to the ocean sink to be 209 years . These new
estimates of the partial lifetimes with respect to soil and oceanic sinks
produce a new total lifetime estimate of 33 years, consistent with the
estimate given in . This longer total lifetime reduces the
discrepancy between the bottom-up and top-down emissions from 54 to 15 Gg yr-1. While the new
bottom-up emission is still less than the top-down emission, the new
estimates reconcile the CCl4 budget discrepancy when considered at
the edges of their uncertainties. A recent study estimated that the average
European emissions for 2006–2014 were 2.3 Gg yr-1, with an average decreasing trend of 7.3 % yr-1.
Since the atmospheric loss of CCl4 is mainly due to photolysis in the
stratosphere, satellite measurements that provide vertical profiles are
particularly useful in validating the stratospheric loss rates in atmospheric
models. A global distribution of CCl4 extending up to the
mid-stratosphere was obtained by the Atmospheric Chemistry Experiment Fourier
transform spectrometer (ACE-FTS; ). This study derived an
atmospheric lifetime of 34 years through correlation with CFC-11. Another
study using ACE-FTS measurements in estimated the CCl4
atmospheric lifetime to be 35 years. A trend in atmospheric CCl4 from
ACE-FTS measurements was reported in , averaged in the
30∘ S–30∘ N latitude belt and
in the altitude range from 5 to 17 km, where it was found to be decreasing at
a rate of 1.2 % yr-1.
In this paper, we report the global atmospheric distribution of CCl4
as a function of altitude and latitude obtained from the measurements of the
limb emission sounder MIPAS (Michelson Interferometer for Passive Atmospheric
Sounding; ) onboard the ENVISAT satellite. The data product
employed here was generated with the processor of the ESA version 7 . MIPAS CCl4 vertical profiles are
compared with correlative independent measurements. The trend in CCl4
as a function of altitude and latitude is also determined. The MIPAS
measurements provide a denser and more complete geographical coverage than
those provided by the ACE-FTS measurements, allowing for a more precise knowledge
of the CCl4 global distribution and of the trend. The key photolytic
loss region (70–20 hPa) is also analyzed.
In Sect. , we introduce MIPAS measurements, the retrieval
setup, and the error budget of the CCl4 profiles. In
Sect. , we discuss the global CCl4 distribution and
the interhemispheric differences determined from MIPAS measurements. In
Sect. , we show the results of the comparisons between
MIPAS and CCl4 correlative measurements from the balloon version of
the MIPAS instrument and the ACE-FTS. In Sect. , we
illustrate the method adopted for the estimation of the atmospheric trends
and the results of trend analysis, along with some comparisons to previously
published results. In Sect. , we evaluate the CCl4
stratospheric lifetime using the tracer–tracer linear correlation method and
compare the results with previously published estimates.
MIPAS measurements
In the first 2 years of operation (from July 2002 to March 2004) MIPAS
acquired, nearly continuously, measurements at full spectral resolution (FR),
with a spectral sampling of 0.025 cm-1. On 26 March 2004, FR
measurements were interrupted due to an anomaly in the movement of the
interferometer drive unit. After instrument diagnosis and tests by the
hardware experts, atmospheric measurements were resumed in January 2005.
After this date, however, MIPAS adopted a reduced spectral resolution of
0.0625 cm-1. Being achievable with a shorter interferometric
scan, measurements with this spectral resolution require a reduced
measurement time compared to the FR, thus allowing a finer spatial sampling.
For this reason, the measurements acquired from January 2005 onward are
referred to as optimized resolution (OR) measurements. Compared to the FR,
they show both a reduced noise equivalent spectral radiance (NESR) and finer
vertical and horizontal spatial samplings. The nominal FR (OR) scan pattern
consists of 17 (27) sweeps with tangent heights in the range from 6 to 68
(7–72) km with 3 (1.5) km steps in the upper troposphere–lower stratosphere
(UTLS) region. Full details of the MIPAS measurements acquired in the two
mission phases are reported in . It is worth mentioning
here that in both mission phases MIPAS measurements cover the whole globe
with a dense sampling, allowing the study of the evolution of atmospheric
composition in great detail. The ESA operational Level 2 algorithm retrieves
target parameters at the tangent points of the limb measurements (or at a
subset of them). The inversion process minimizes the χ2 function,
using the Gauss–Newton iterative scheme with the Marquardt modification. An
adaptive a posteriori regularization is used in order to smooth the profiles
with a strength determined on the basis of the error bars of the
unregularized profile .
The ESA Level 2 processor version 7 retrieves CCl4 volume mixing
ratio (VMR) profiles along with a set of other target parameters.
The retrieval is based on the fit of a set of narrow (3 cm-1)
spectral intervals called microwindows (MWs) containing relevant information
on the target parameters. As for all MIPAS ESA retrievals, the MWs for
CCl4 retrievals are selected with the MWMAKE algorithm
. This algorithm identifies the spectral intervals to be used
in the inversion, with the aim of minimizing the total retrieval errors
(including both systematic and random components). The MWs used in the ESA
Level 2 retrievals from nominal FR and OR measurements are listed in
Table .
CCl4 VMR is retrieved only up to about 27 km since above this
altitude the CCl4 concentration is too small to generate a sufficient
contribution to the measured spectrum for analysis. Moreover, OR measurements
sample the limb with a vertical step of 1.5 km, significantly finer than the
instrument field of view (≈3 km). For this reason, to avoid numerical
instabilities due to oversampling, in the inversion of OR measurements the
retrieval grid includes only one out of every two tangent points.
Figure characterizes a typical CCl4 retrieval from
nominal limb scans acquired in the FR (top panel) and OR (bottom panel)
measurement phases. The colored solid lines show the rows of the averaging
kernels (AKs), each row corresponding to a retrieval grid point (eight grid
points for FR and seven for OR retrievals). Typically the number of degrees of freedom (DoFs) of the retrieval (trace of the AK matrix) is 5–6 for FR and 4–5 for
OR measurements. The slightly smaller number of DoFs obtained
in the OR retrievals stems from the fact that, to make the retrieval more
stable, CCl4 is not retrieved at every tangent point of the OR limb
measurements. The dotted red line in Fig. represents the
vertical resolution, calculated as the full width at half maximum (FWHM) of the
AK rows.
Typical averaging kernels (AKs, colored solid lines) and vertical
resolution (red dotted lines) of CCl4 VMR retrieved from full resolution (FR,
a) and optimized resolution (OR, b) MIPAS measurements. The vertical
resolution is calculated as the FWHM of the AK rows. The plot's key also shows
the average number of degrees of freedom (DoFs) of the retrieval (trace
of the AK matrix) and the number of retrieval grid points
(Npt).
Microwindows (MWs) used for CCl4 retrieval from nominal FR
and OR MIPAS measurements.
MWs used in CCl4 retrievals from FR measurements Start wave number (cm-1)End wave number (cm-1)796.3750799.3750800.2750803.2750792.7000795.7000771.8000773.7750MWs used in CCl4 retrievals from OR measurements Start wave number (cm-1)End wave number (cm-1)792.8125795.8125Error budget
To evaluate the CCl4 VMR error due to the mapping of the measurement
noise in the retrieval, we use the error covariance matrix provided by the
retrieval algorithm . The other error components
affecting the individual CCl4 VMR profiles are evaluated at Oxford
University using the MWMAKE tool. Figure summarizes the
most relevant error components affecting each individual retrieved
CCl4 profile, using the MWs of Table , for both the FR
(top panel) and OR (bottom panel) nominal MIPAS measurement cases.
Main error components of the individual retrieved CCl4 VMR
profiles from FR (a) and OR (b) nominal MIPAS
measurements.
The key “RND” in the plots refers to the mapping of the measurement noise
in the retrieval, as evaluated for typical FR and OR retrievals. Apart from
the “NLGAIN” error that will be discussed later, the other error
components, in both the FR and OR cases, can be grouped as follows: (a) the
errors due to the uncertainties in the (previously retrieved) pressure and
temperature profiles (PT) and VMR of spectrally interfering gases, for
example O3, H2O, HNO3, and NH3; (b) the error
due to horizontal variability in the atmosphere (GRAD) not included in the
model; (c) the uncertainties in the spectroscopic (SPECDB) and cross-section
(LUT) databases and the error in the CO2 line mixing model (CO2MIX); (d) the errors due to less-than-perfect instrument line-shape characterization,
namely its spectral shift (SHIFT) and width (SPREAD). For the details on how
the different error components were calculated by MWMAKE, see
and the Oxford University MIPAS website .
The main errors of type (a) are due to interfering gases whose VMRs are
retrieved before CCl4 with some random error. Therefore, like the RND
error component, they change randomly from profile to profile. Thus, in the
calculated (monthly) averages they scale down with the inverse square root of
the number of averaged profiles. The errors of type (b), as shown in
, cause systematic (and opposite in sign) differences
between profiles retrieved from measurements acquired in the ascending and
the descending parts of the satellite orbits. These errors largely cancel out
when calculating averages that evenly include profiles retrieved from
measurements belonging to the ascending and the descending parts of the
orbits. Errors of type (c) are constant and may cause profile biases but have
no effect on calculated trends. Regarding the errors due to the imperfect
instrument line-shape modeling (type d), since the gain of MIPAS bolometric
detectors remained constant throughout the whole mission, there is no hint of
a possible degradation of instrument optics and thus of a possible change in
the instrument line shape. This type of error, therefore, has no impact on
the trend calculation.
Imperfect instrument radiometric calibration also causes an error. This error
is plotted in Fig. with the label NLGAIN. Being of
the order of 0.4 % in the upper part of the retrieval range, it is rather
small in individual CCl4 profiles. Although small, this error is
important when calculating atmospheric trends as it includes the uncertainty
in the correction applied to the radiances to account for the nonlinearities
of MIPAS photometric detectors . In MIPAS Level 1b
radiances up to version 5, the applied nonlinearity correction is constant
throughout the whole MIPAS mission. However, nonlinearities change over the
course of the mission due to progressive aging of the detectors. A constant
correction therefore implies a drift of the radiometric calibration error
during the mission, with a direct impact on the calculated trends. MIPAS
Level 1b radiances version 7 overcome this problem as they use a
time-dependent nonlinearity correction scheme. The residual drift of the
calibration error after this time-dependent correction is still being
characterized; however, preliminary results (M. Birk, personal communication, 2016) show that
it is smaller than 1 % across the entire mission. MIPAS Level 1b radiances
version 5 were used in the past to extract information on trends of different
gases, either ignoring this effect (see, e.g., CFC-11/CFC-12 in
, or HCFC-22 in ) or correcting the drift
via intercomparison with other instruments assumed to be drift-free
. Recently it has been shown that ignoring
this effect introduces a significant error on the trend estimation. The MIPAS
Level 1b calibrated radiances version 7 employed here are considered to be a
significant improvement from the point of view of the correction of this
drift.
The generally good quality of fits obtained in CCl4 retrievals is
illustrated in Fig. . The figure refers to the MWs used in
the FR retrievals. We do not show the residuals in the single MW used for OR
retrievals as it mostly overlaps the third MW of FR retrievals. The upper
plot of Fig. shows the average of 1141 observed (black
dots) and simulated (red line) limb radiances in the MWs used for
CCl4 retrievals. The averages include spectra with tangent heights ranging from 6 to 17 km. The lower plot shows the average residuals of the
fit (observation minus simulation, blue line) as well as the average noise
level of the individual MIPAS measurements (dashed lines). The grey areas
indicate spectral channels that, as recommended by the MWMAKE algorithm, are
excluded from the fit to minimize the total retrieval error. Note that the
average residuals shown in Fig. have an associated random
error given by the noise of the individual measured spectra divided by the
square root of the number of averaged spectra, i.e., ≈ 1 nW/(cm2sr cm-1). This implies that while the magnitude of the average
residuals is incompatible with their noise error, the additional systematic
uncertainties are still smaller than the noise error of the individual
measured spectra, in agreement with the predictions reported in
Fig. .
Panel (a) shows an average of 1141 observed (black dots) and
simulated (red line) limb radiances in the MWs used for CCl4 FR
retrievals. The averages include spectra with tangent heights from 6 to
17 km. Panel (b) shows the average residuals of the fit (blue line,
observation minus simulation) as well as the average noise level of the
individual measurements (dashed lines). The grey areas indicate spectral
channels excluded from the fit. The radiance units (r.u.) in the vertical
axes of the plots are nW/(cm2 sr cm-1).
CCl4 global distribution
Figure shows the global monthly distribution of MIPAS
CCl4 VMR for a representative month from each of the four seasons,
spanning the time period from August 2010 through May 2011. Here, retrieved
profiles were first interpolated to fixed pressure levels (see Sect. ) and then binned in 5∘
latitude intervals. In all the considered months, the zonal averages show the
typical shape of long-lived species of anthropogenic origin, which are
emitted at the surface and destroyed primarily in the stratosphere. Larger
values are found in the troposphere, and then the VMR monotonically decreases
with increasing altitude in the stratosphere. In the lower stratosphere,
concentrations between 30∘ S and
30∘ N are significantly larger compared to those at
higher latitudes. This pattern can be attributed to the Brewer–Dobson
circulation that is responsible for the uplift of the surface air in the
tropical regions.
Zonal monthly averages of MIPAS CCl4 profiles. The maps
refer to four separate months in different seasons: August 2010 (a),
November 2010 (b), February 2011 (c), and May 2011 (d).
The maps in Fig. show the time evolution of CCl4 at
all latitudes from July 2002 to April 2012. The three maps refer to different
pressure levels: 50 hPa (upper map), 90 hPa (middle map), and 130 hPa (lower
map). The CCl4 time evolution maps show a seasonal variability. The
intrusion of CCl4-poor mesospheric air in the stratosphere during
winter, due to the air subsidence induced by the polar vortex, is clearly
visible in both polar winters, its effects continuing into early spring and
extending into the troposphere. Minimum CCl4 values are observed in
November at the South Pole and in March at the North Pole (November is
considered the beginning of spring at the South Pole, whereas spring begins
in March at the North Pole). This was previously observed for other long-lived anthropogenic species . The effect is larger in the
Antarctic due to the stronger, more stable polar vortex. Modulated by this
seasonal variability, at all altitudes a constant trend and an
interhemispheric difference can also be observed and are further analyzed
in the subsequent figures. We also note that for pressures larger than
100 hPa, the CCl4 measured in the OR phase has a positive bias with
respect to that measured in the FR phase. This bias, discussed also in
Sect. , may be due to the different MWs used for
the retrieval in the two mission phases or to the different limb sampling
patterns adopted.
Time evolution of CCl4 at all latitudes, from July 2002 to April 2012. The three maps refer
to different pressure levels: 50 hPa (a), 90 hPa (b), and 130 hPa (c).
The vertical dashed lines represent the year boundaries.
Average north–south CCl4 VMR differences versus latitude and
pressure. The average period includes MIPAS measurements from 1 April
2005 to 31 March 2012.
The IHG at the surface is largely used as a
qualitative indicator of continuous emissions .
Anthropogenic emissions are larger in the Northern Hemisphere (NH;
) and the transport of these emissions from the NH to the
Southern Hemisphere (SH) takes about 1 year, i.e., a time interval much
shorter than the CCl4 lifetime (see Sect. ). Hence,
a significant IHG in the CCl4 distribution represents evidence of
ongoing emissions.
Although MIPAS measurements are not suitable for evaluating the IHG at the
surface, they provide information about the distribution of interhemispheric
differences in the UTLS region as a function of both latitude and pressure.
To analyze these differences, we interpolated to a fixed pressure grid MIPAS
CCl4 profiles acquired from April 2005 to March 2012. We then binned
the profiles in 5∘ latitude intervals and calculated,
for each latitude bin, the average CCl4 VMR profile in the considered
time period. Finally, for each latitude bin in the NH we identified the
corresponding bin in the SH and computed the difference between the average
profiles. The map of Fig. shows the
obtained average differences as a function of both latitude bin and pressure
level. At high latitudes, the asymmetry likely stems from the fact that the
polar vortex in the Antarctic is systematically stronger, more stable, and of
longer duration than the Arctic polar vortex. At midlatitudes, NH and SH
seasons are more symmetrical and the CCl4 mean differences between
the two hemispheres are probably caused by the larger CCl4 emissions
in the NH . As a final test we computed the weighted
average of the NH–SH differences over latitude at fixed pressure levels. The
weights used in the average are the solid angle fractions viewed by the
individual latitude bands. The NH–SH mean differences in the UTLS span from
1.2 ppt at 130 hPa to 2.2 ppt at 100 hPa. At the lowermost pressure levels
these differences are fully consistent with the IHG value of
1.5 ± 0.2 ppt (for 2000–2012) reported by .
Comparison to other CCl4 measurements
The most accurate atmospheric CCl4 measurements are collected at
ground level, but such measurements are not suitable for direct comparison
with profiles retrieved from MIPAS measurements in the 5–27 km height range.
In the next two subsections we compare MIPAS CCl4 profiles with
co-located profiles obtained from the stratospheric balloon version of MIPAS
(MIPAS-B; ) and from the ACE-FTS onboard the SciSat-1
satellite .
Comparison with MIPAS balloon
The balloon-borne limb emission sounder MIPAS-B can be regarded as a
precursor of the MIPAS satellite instrument ( and references
therein). Indeed, a number of specifications like spectral resolution
(0.0345 cm-1) and spectral coverage
(750–2500 cm-1) are similar. However, for other parameters
the MIPAS-B performance is superior, in particular for the NESR and for the
line-of-sight stabilization, which is based on an inertial navigation system
supplemented with an additional star reference system and leads to a
knowledge of the tangent altitude on the order of 90 m (3σ). The
MIPAS-B NESR is further improved by averaging multiple spectra recorded at
the same elevation angle. MIPAS-B limb scans are typically acquired on a
1.5 km vertical tangent height grid.
Retrieval of all species is performed on a 1 km grid with a least squares
fitting algorithm using analytical derivative spectra calculated by the
Karlsruhe Optimized and Precise Radiative transfer Algorithm
. To avoid retrieval instabilities due to
oversampling of vertical grid points, a regularization approach is adopted,
which constrains with respect to a first-derivative a priori profile according to
the method described by Tikhonov and Phillips. The spectral window used for
the MIPAS-B target parameter retrieval of CCl4 covers the
786.0–806.0 cm-1 interval. Spectroscopic parameters for the
calculation of the infrared emission spectra are a combination of the HITRAN
2008 database and the MIPAS dedicated database
. The CCl4 cross sections are taken from
HITRAN as in MIPAS/ESA retrievals version 7. The MIPAS-B error budget
includes random noise as well as covariance effects of the fitted parameters,
temperature errors, pointing inaccuracies, errors of non-simultaneously
fitted interfering species, and spectroscopic data errors (1σ). For
CCl4 the precision error is estimated to be between 5 and 10%, while the
total error is 11–15 %. Further details on the MIPAS-B data analysis and
error estimation are provided in and references therein.
Table lists all the MIPAS-B flights used for
intercomparison with MIPAS on ENVISAT.
Overview of MIPAS balloon flights used for intercomparison with
MIPAS/ENVISAT.
LocationDateDistance (km)Time difference (min)Kiruna (68∘ N)20 Mar 200316/54614/153 Jul 2003Trajectories only11 Mar 2009187/2485/624 Jan 2010109/3025/631 Mar 2011Trajectories onlyAire-sur-l'Adour (44∘ N)24 Sep 200221/588/410/14612/13/15/16Teresina (5∘ S)14 Jun 2005109/497/184/338228/229/268/2696 Jun 2008224/284/600/194157/158/169/170
Furthermore, to the direct matches in which the balloon and the satellite instruments
observe (within pre-defined margins) the same air masses simultaneously, we
also considered trajectory matches. In this case both forward and backward
trajectories were calculated by the Free University of
Berlin from the balloon measurement geolocation to search for air masses
sounded by the satellite instrument. Temperature and VMR values from the
satellite profiles were interpolated to the trajectory match altitude such
that these values can be directly compared to the MIPAS-B data at the
trajectory start point altitude. To identify both direct and trajectory
matches, a coincidence criterion of 1 h and 500 km was adopted.
Intercomparison between MIPAS-B and MIPAS/ENVISAT (MIPAS-E) CCl4
VMR. Results for the FR part of the MIPAS mission. The plots show mean
absolute and relative VMR differences of trajectory match collocations (red
numbers) between both MIPAS sensors (red solid line) including standard
deviation of the difference (red dotted lines) and standard error of the mean
(plotted as error bars). Precision (blue dotted lines), systematic (blue
dashed, dotted lines), and total (blue dashed lines) mean combined errors
calculated according to the error summation σMIPAS-E2+σMIPAS-B2 are also displayed. For further details on the error
calculation, see .
Same as Fig. but
for the OR part of the MIPAS mission.
Figures and
show the average differences between CCl4 VMR retrieved from
MIPAS/ENVISAT and MIPAS-B both in absolute and relative units. The two
figures refer to matching measurements in the FR and the OR phases of the
MIPAS/ENVISAT mission, respectively. Combined random, systematic, and total
errors are also shown in the plots. The numbers reported on the left side of
the plots indicate the number of matching profiles contributing to the
statistics. The results of the intercomparison can be summarized as follows.
In the case of FR measurements: for pressures between 80 and 190 hPa
MIPAS/ENVISAT shows a statistically significant negative bias of about
-10 % with respect to MIPAS-B; this bias is however within the combined
total error bounds. A statistically significant positive bias is also evident
for pressures smaller than 25 hPa. It increases with altitude and quickly
becomes incompatible with the total combined error. This bias can be at least
partly explained by the selection of different microwindows used during the
retrieval process of both MIPAS sensors. This bias, however, is not a major
concern because it is localized at the upper end of the retrieval range. In
this region the predicted uncertainty is so large that the linear
approximation of the error propagation theory may easily fail to explain the
discrepancies between the measurements of the two instruments. In case of OR
measurements: for pressures between 150 and 190 hPa MIPAS/ENVISAT shows a
statistically significant positive bias of about +10 % with respect to
MIPAS-B; this bias is however within the combined total error bounds. A
statistically significant positive bias is also evident for pressures smaller
than 25 hPa. It increases with altitude and, for pressures smaller than
20 hPa, is no longer compatible with the total combined error. As in the FR
case, this large bias occurs at the upper end of the MIPAS/ENVISAT retrieval
range at which the predicted combined error is very large. Furthermore,
comparison with ACE (see next Section) indicates a negative bias of MIPAS
with respect to ACE-FTS in the same altitude region; hence, MIPAS/ENVISAT is
in the middle between the MIPAS balloon and ACE-FTS.
Comparison with ACE-FTS v3.5
ACE-FTS is a Canadian solar occultation limb sounder operating since 2004
from SciSat in a low (≈650 km) circular orbit. The measured spectra
cover the region from 750 to 4400 cm-1 with a spectral resolution of
0.02 cm-1. Several target atmospheric parameters are
routinely retrieved from ACE-FTS measurements. Among them are temperature,
pressure, and the VMR profiles of over 30 atmospheric trace gases and over 20
subsidiary isotopologues. Profiles are retrieved in the range from ∼ 5
to 150 km, with a vertical field of view of ∼ 3–4 km and a vertical
sampling of 2–6 km. The ACE-FTS retrieval algorithm is described in
, and the updates for the most recent version of the retrieval,
version 3.5, are detailed in . The retrieval algorithm uses a
nonlinear least-squares global-fitting technique that fits the ACE-FTS
observed spectra in given microwindows with forward modeled spectra based on
line strengths and line widths from the HITRAN 2004 database
(; with updates as described by ). Pressure and
temperature profiles used in the forward model are the profiles derived from ACE-FTS, calculated by fitting CO2 lines. The spectral window used for
CCl4 retrievals extends from 787.5 to 805.5 cm-1.
Several hundred ACE-FTS measurements are coincident with MIPAS soundings of
the OR part of the mission. These measurements are located both in the
Northern and Southern hemispheres, mainly at latitudes larger than
45∘. For comparison with MIPAS, all ACE-FTS
CCl4 data used were screened using the v3.5 quality flags. As
recommended by , any profile data point with a flag value of 2
or greater was removed and any profile containing a flag value between 4 and
7, inclusive, was discarded. For intercomparison with MIPAS measurements we
adopted a matching criterion of 3 h and 300 km. We also tested different
matching criteria, such as 2 h and 300 km and 3 h and 200 km, but found
no significant changes in the intercomparison. First we interpolated the
matching MIPAS and ACE-FTS CCl4 profiles to a fixed set of pressure
levels. Then we grouped the profile differences in latitudinal intervals. The
results of the comparison are summarized in Fig. .
Each of the four plots of the figure refers to one of the considered latitude
intervals: 50–70 and
70–90∘ in both the Southern and the Northern
hemispheres. Each plot shows the average CCl4 difference profile
between co-located MIPAS and ACE-FTS measurements (red) with standard
deviation of the mean (red error bars, calculated as the standard deviation
of the differences divided by the square root of the sample size). The
standard deviation of the differences (orange), the total random error
(green), and the total systematic error of the difference (blue) are also shown.
The number of co-located pairs contributing at each pressure level is
reported on the right side of each plot. The average difference (red line)
quantifies the systematic bias between ACE-FTS and MIPAS; the error bars
indicate its statistical significance. The standard deviation (orange) is an
ex-post estimate of the combined random error of the individual
profile differences and should therefore be similar to its ex-ante
estimate represented in the plots by the green line. We calculated the
ex-ante random error of the individual profile differences as the quadrature
summation of the ACE-FTS and MIPAS random errors. The ACE-FTS random error is
estimated via the noise error covariance matrix of the retrieval included in
the Level 2 products. The MIPAS random error is estimated as the quadrature
summation of the measurement noise error evaluated by the covariance matrix
of the retrieval and the other error components that are
expected to change randomly in our sample, i.e., the errors that we classified
into types (a) and (b) in Sect. . The systematic error of
the profile differences is obtained as the quadrature summation of the
ACE-FTS and the MIPAS errors that are constant within the sample and are not
expected to bias in the same direction as the measurements of the two
instruments. On the basis of the error figures suggested by ,
for ACE-FTS we assumed a 20 % systematic error constant at all pressure
levels. For MIPAS we calculated the quadrature summation of systematic errors
that in Sect. we classified as of types (c) and (d). For
the calculation of the combined systematic error we explicitly excluded the
uncertainty in the CCl4 cross-section data that are
used, approximately in the same spectral region, both in MIPAS and ACE-FTS
retrievals.
Mean CCl4 profile difference between co-located MIPAS and
ACE-FTS measurements (red) with standard deviation of the mean (red error
bars). The standard deviation of the differences (orange), the estimated
total random (green), and total systematic (blue) errors of the difference are
also shown. The number of co-located pairs for each pressure level is
reported on the right side of each graph. Each plot refers to a latitude
interval as indicated in the title.
Apart from the latitude interval from 50 to 70∘ S,
the systematic differences between MIPAS and ACE-FTS are within 5 pptv (∼10 %, mostly not significant from the statistical point of view) in the
pressure range from 50 to 100–110 hPa. The amplitude of systematic
differences increases up to 15–20 pptv and becomes statistically significant
at 30 hPa, while it is again quite small at 20 hPa. In the latitude interval
from 50 to 70∘ S we observe a statistically
significant ≈ 10 pptv low bias of MIPAS with respect to ACE-FTS,
almost uniform over the entire retrieval height range. At all latitudes, the
observed biases are compatible with the estimated combined systematic error
only for pressures greater than 40 hPa. At 30 hPa the bias is statistically
significant and incompatible with error bars. The reason for this
inconsistency is still unclear; however, preliminary investigations show that
the inconsistency will be reduced when using the future release version 4.0
of ACE-FTS products.
The ex-ante estimate of the combined random error (green line in
Fig. ) agrees pretty well with the ex-post estimated
standard deviation of the profile differences (orange line) in the range
between 40 and 80–100 hPa. At the limits of the retrieval range the observed
variability in the differences generally exceeds the ex-ante estimate of the
random error. This may be due both to the fact that our ex-ante random error
estimate does not take into account the imperfect matching of the compared
profiles and to the fact that, at these specific altitudes, the sensitivity
of the measurements to the CCl4 VMR is so low that the linear
approximation of the error propagation theory could provide only rough error
estimates.
As a final remark we note that at 30 hPa, MIPAS-B
(Fig. ) and ACE-FTS
(Fig. ) intercomparisons provide contrasting
indications on the MIPAS bias in the OR part of the mission. While MIPAS-B
suggests a positive MIPAS bias of about 10 pptv, ACE-FTS points to a
negative bias of 10–20 pptv.
TrendsTrend calculation method
The measurements used for the analysis presented in this study cover the
entire MIPAS mission, from July 2002 to April 2012. The CCl4 VMR
profiles considered are those derived by the ESA Level 2 processor version 7
analyzing MIPAS limb scanning measurements with tangent heights in the
6–70 km range, obtained from nominal (NOM), middle atmosphere (MA), and upper
troposphere–lower stratosphere observational modes
(UTLS1, Raspollini et al., 2013).
First we linearly interpolate in log pressure all the considered CCl4
VMR profiles to the 28 SPARC data initiative pressure
levels (300, 250, 200, 170, 150, 130, 115, 100, 90, 80, 70, 50, 30, 20, 15,
10, 7, 5, 3, 2, 1.5, 1.0, 0.7, 0.5, 0.3, 0.2, 0.15, 0.1 hPa). We then group
the interpolated profiles in 5∘ latitude bins and
calculate monthly averages. Finally, using the least-squares method, for each
latitude bin and pressure level we fit the following function VMR(t)
to the time series of the monthly averages:
In this expression t is the time expressed in months since the beginning of
the mission (July 2002) and aFR, aOR, b, f1, f2,
g, and ci and di with i=1,…,8 are the 22 fitting parameters. The function
1P(t) is the indicator function of the time interval P,
such that 1P(t)=1 if t∈P and 1P(t)=0 otherwise. The functions qbo30(t) and qbo50(t) are
the quasi-biennial oscillation (QBO) quantifiers and SRF(t) is the
solar radio flux index. The two QBO terms (available at
http://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/index.html)
represent the Singapore winds at 30 and 50 hPa . The
SRF index is calculated using measurements of the solar flux at
10.7 cm (available at
http://lasp.colorado.edu/lisird/tss/noaa_radio_flux.html) and is
considered a good proxy for the solar activity. We renormalized both the QBO
and the SRF proxies to the interval [-1,+1] within the time frame covered
by MIPAS mission. The terms in the sum are eight sine and eight cosine functions.
They represent periodic oscillations with period Ti. In Ti we include
annual (12 months), semi-annual (6 months), and other characteristic
atmospheric periodicities of 3, 4, 8, 9, 18, and 24 months .
We decided to fit two different constant parameters for the two parts of the
mission: aFR for the FR and aOR for the OR part. The aim of
this choice is to account for possible relative biases between the two phases
of the mission. These may be caused, for example, by the different spectral
resolutions adopted, by the different MWs used for the retrieval, and by the
different vertical and horizontal samplings of the instrument in the two
mission phases. We calculate the uncertainty on the fitted parameters
assuming that each monthly average is affected by an error given by the standard
deviation of the mean. Furthermore, we multiply the uncertainty obtained from
the error propagation analysis by the square root of the normalized least
squares (the so-called reduced χ2). This latter operation is
intended to also account for the quality of the fit in the evaluation of
trend errors.
Results
Figure shows some examples of CCl4 trend analysis.
Each panel refers to a specific latitude band and pressure level. The top
plot of each panel shows the time series of the monthly averages with error
bars given by the standard deviation of the mean (blue symbols). The red
curve represents the best fitting function VMR(t), while the green
line represents the constant and the linear (trend) terms of VMR(t).
In the lower plot of each panel we show the residuals of the fit (the monthly
averages minus the values calculated on the fitting curve). In each panel we
also report the value obtained for the trend, its uncertainty and the
difference between the two constant terms aFR-aOR.
CCl4 trend analysis for 20–25∘ S at 50 hPa (a),
55–60∘ S at 100 hPa (b), 25–20∘ N at 90 hPa (c), and 50–45∘ N
at 100 hPa (d). The blue dots are the MIPAS monthly averages and
the error bars are the standard deviation of the means. The red curve is the
best fitting function VMR(t) and the green line is the linear term
(trend). The lower part of each plot shows the residuals between the MIPAS
monthly averages and the best fitting function VMR(t). The
CCl4 trend, its uncertainty, and the bias between FR and OR are
also indicated in each panel.
The quality of the fit is generally better in the OR period. Indeed, in this
mission phase the instrument provides measurements with more uniform and
finer geographical coverage. We also carried out a spectral analysis of the
fitting residuals, which revealed that all the periodicities embedded in the
considered time series of monthly means are properly accounted for by the
fitting function ().
CCl4 trends as a function of latitude and pressure. Panel
(a) shows absolute trends, (b) percentage trends, (c) absolute errors, and (d) percentage
errors. Latitudes and pressures with a trend error greater than 30 % are masked
with dashed areas.
Figure summarizes the results obtained for CCl4
trends. Panel (a) shows the absolute trends. Negative trends are observed at
all latitudes in the UTLS region. The magnitude of the negative trend
decreases with increasing altitude. The trend shows slightly positive values
(about 5–10 pptv decade-1) in a limited region, particularly in the Southern
midlatitudes between 50 and 10 hPa. This feature is probably related to the
asymmetry in the general circulation of the atmosphere. The air at higher
altitudes can be considered older than the tropospheric air that has
been lifted up by strong convection mechanisms in the tropical regions
. The tropospheric air just injected into the stratosphere
is richer in CCl4. We attribute positive stratospheric trend values
in certain latitude regions to the less effective mixing mechanisms in the
stratosphere as compared to the troposphere at these latitudes. Similar
features have also been observed by other authors in CFC-11 and CFC-12 trends
. Recently some studies have shown that the trends in stratospheric trace gases are
affected by variability in the stratospheric circulation. This has been shown
for a number of halogen source gases and their complementary degradation
products (i.e., HCl and HF). This variability can partially
explain why the stratospheric trend does not simply follow the tropospheric
trend with a time lag.
Assuming the average CCl4
VMR obtained from the full MIPAS dataset for each latitude bin and pressure level, we also calculated the relative
CCl4 trends. They are shown in panel (b) of
Fig. . The same considerations made for the absolute
trends also apply to relative trends. The asymmetry between the NH and the SH
is very pronounced, the NH having larger negative relative trends increasing
with altitude and reaching 30–35 % decade-1 at 50 hPa. Note however that above
50 hPa they show large variations with both latitude and pressure. These
oscillations correspond to extremely small average VMR values that make the
relative trend numerically unstable. Panels (c) and (d) of
Fig. show, respectively, the absolute and percentage
random errors on the trends. The uncertainties increase above 20 hPa. Large
uncertainties are associated with latitude bins and pressure levels for which a
relatively small number of measurements is available.
Map of the ratio between CCl4 trends and associated random
errors.
For clarity in Fig. we show the ratio between
CCl4 trends and the related random errors. Ratio values less than 2, marked with white
and grey colors, correspond to trend values that are
not significantly different from zero from a statistical point
of view. Note, however, that most of the
calculated trends are greater than 5 times the related error and are thus
statistically significant. In the maps of Figs.
and , values corresponding to errors greater than
30 % are masked with dashes. We consider any trends with errors
greater than this threshold to be unreliable.
As mentioned in Sect. , an important source of
uncertainty could arise from a residual drift of the calibration error,
possibly due to neglecting changes in detector nonlinearity as the
instrument ages. As outlined in Sect. , however, the
worst-case scenario for the drift of the calibration error could amount to
1 % of the calibration error itself, which in turn, is of the order of
0.4 %
of each individual retrieved CCl4 VMR profile. Therefore, this error
source is negligible compared to the statistical error shown in panel (d) in
Fig. .
Comparison with CCl4 trends reported in literature
Although measurements acquired at ground stations cannot be directly compared
with MIPAS profiles that have a lower altitude limit of 5–6 km, we can still
compare tropospheric CCl4 trends derived from MIPAS with trends
derived from ground-based measurements. Under the assumption of a well-mixed
troposphere, we can consider the CCl4 vertical distribution
to be approximately constant . We consider
observations provided by two networks that regularly perform long-term,
highly accurate near-surface measurements of various tracers, including
CCl4: the NOAA/ESRL/HATS (http://www.esrl.noaa.gov/gmd/hats/)
and the AGAGE (; http://agage.mit.edu/)
networks. The NOAA/ESRL/HATS group provides accurate measurements of
CCl4 through three different programs: two in situ electron capture
detector (ECD) measurement programs and one flask system using gas
chromatography with the ECD program. In this work we use a CCl4 combined
dataset, developed by NOAA to homogenize all of the measurements made by
the different programs (more details at
http://www.esrl.noaa.gov/gmd/hats/combined/CCl4.html). All the
CCl4 NOAA records are reported on the NOAA-2008 scale. AGAGE
measurements used here are obtained using in situ gas chromatography with ECD
and reported on the SIO-2005 calibration scale. NOAA and AGAGE in situ
measurements at common sites are intercompared every 6 months for validation
purposes.
To compare MIPAS CCl4 trends to those derived from the ground-based
measurements of NOAA and AGAGE, we first choose a pressure level belonging to
the troposphere, with the following procedure. For each latitude bin
(λ) and MIPAS monthly average profile we identify the tropopause with
the pressure level at which the monthly average temperature shows its minimum
value. We multiply this pressure by 1.6 and find the nearest pressure level
(pt(λ)) in the fixed pressure grid defined in
Sect. . Using this procedure the selected pressure
level is located approximately 3 km below the tropopause. For each latitude
bin and month we then compute the monthly CCl4 average at pt(λ). Finally, for each latitude bin, we calculate the trend at this
month- and latitude-dependent tropospheric pressure as explained in
Sect. .
Figure compares the time series of ground-based
CCl4 measurements of selected stations (black and orange lines) with
MIPAS monthly tropospheric averages (blue dots) in the same latitude bin of
the ground station. The two plots refer to ground stations located at
tropical (top) and middle (bottom) latitudes. Ground-based measurements do
not really show a seasonality, while MIPAS measurements do. The amplitude of
the seasonal variations observed by MIPAS increases with latitude. For
tropical latitudes MIPAS OR measurements show a positive bias of
approximately 15 %. Although not focused on tropical regions,
Fig. comparing MIPAS to balloon measurements
already suggests the existence of this bias. At middle latitudes the maximal
values of the MIPAS time series roughly match ground measurements. In
Fig. we also show the trend values determined on the
basis of the plotted measurements. In the examined cases the trends obtained
from MIPAS and ground stations are in very good agreement.
Comparison between MIPAS (blue dots) and NOAA/AGAGE (black/orange) CCl4
time series. The two plots refer to ground stations located at tropical (a) and middle
(b) latitudes. The red curve is the fitting model used to derive the trend from MIPAS
data; the green line is the linear part of the model itself. The obtained trend values are
also shown in the plots.
In Table we compare MIPAS tropospheric CCl4 trends
with trends derived for the 2002–2012 decade from NOAA/AGAGE stations
located in the same latitude band. Some stations produce CCl4 trends
in very good agreement with MIPAS. However, in general, and especially in the
polar regions, the variability in the tropopause is quite large, thus
producing time series of MIPAS monthly averages at pt(λ) that
can not be adequately matched by the fitting function defined in
Eq. (). This feature sometimes generates large residuals
in the trend fit and thus large trend errors and/or unrealistic trend values.
Despite this difficulty, from the statistical point of view the only trends
calculated at the CGO site disagree significantly. We attribute this
disagreement to the instabilities occurring in MIPAS data at low altitudes.
Indeed, the MIPAS tropospheric trend estimated for the latitude bin
35–40∘ S (the bin adjacent
to the CGO site) is already equal to -9.16±2.03 pptv decade-1, i.e., in
perfect agreement with the trend calculated from the CGO measurements.
For each ground station the table columns show respectively: site
code, site name, site latitude, network name, station-related CCl4
trend, tropospheric MIPAS trend, and latitudinal band from which MIPAS data were
extracted.
SiteSite nameLatitudeNetworkIn situMIPASMIPAScode(∘N)trendtrendlat. band(pptv decade-1)(pptv decade-1)(∘N)BRWBarrow,71.3NOAA-12.7-3.2±10.470 to 75USAMHDMace Head,53.3AGAGE-10.1-4.7±5.150 to 55IrelandTHDTrinidad Head,41.1AGAGE-10.6-10.2±3.140 to 45USANWRNiwot Ridge,40.4NOAA-12.3-10.2±3.140 to 45USAMLOMauna Loa,19.5NOAA-12.2-14.9±2.315 to 20USARPBRagged Point,13.2AGAGE-10.7-12.7±3.610 to 15BarbadosSMOTatuila,-14.4NOAA-11.8-12.0±3.0-10 to -15American SamoaAGAGE-10.1CGOCape Grim,-40.7AGAGE-10.2-25.9±5.4-40 to -45TasmaniaSPOSouth Pole,-90.0NOAA-11.9-7.9±10.6-85 to -90Antarctica
Looking at the literature, we found that estimate the global
CCl4 trend from ACE-FTS measurements. The authors consider
CCl4 VMR profiles obtained from ACE-FTS in the
30∘ S–30∘ N latitude belt.
They calculate yearly averages of CCl4 VMR in the altitude range from
5 to 17 km and fit the seven 2004–2010 yearly averages with a linear
least-squares approach. The resulting trend is -13.2±0.9 pptv decade-1. If
we average MIPAS trends presented in Sect. in the
30∘ S–30∘ N latitude interval
and in the 100–300 hPa pressure range, with a filter discarding trend values
with relative error greater than 30 %, we get an average trend of
-12.80±0.12 pptv decade-1. This value is in very good agreement with the
trend determined from ACE-FTS. Note also that, since MIPAS measures
the atmospheric emission spectrum, its sampling is finer than that of ACE-FTS both in space
and time. With MIPAS it is therefore possible to estimate trends with a
better precision.
Lifetime
In this section, we estimate the stratospheric lifetime of CCl4
according to the tracer–tracer correlation method established by
based on the theoretical framework presented by
and . Here we choose CFC-11 as the reference tracer
(b) correlated to CCl4 (tracer a). The stratospheric lifetime can
be calculated using the following equation:
τaτb=σa‾σb‾dσadσb|tropopause,
in which τa and τb are the stratospheric lifetimes of the two
correlated tracers and σa‾, σb‾, and
dσa/dσb are, respectively, the atmospheric VMRs of the two
species and the slope of the correlation at the tropopause in steady state. A
major complication that arises when using Eq. () is due the fact that
the considered tracers decline in the 2002–2012 decade; therefore, MIPAS
measurements can not be considered as referring to a steady state. Using decadal
averages for σa‾ and σb‾ does not
actually cause large errors in τa; however, replacing the steady-state
slope with the measured slope dχa/dχb may be a rough approximation
. The difference between the slopes in steady and transient
states is mainly linked to the tropospheric change rate γ0 of the
tracers in the considered time period. In order to account for the effect of
γ0 on dσa/dσb, we use the following formula proposed by
:
dσadσb|tropopause=dχdχb|tropopause⋅dχbdΓ|Γ=0+γ0aσ0adχbdΓ|Γ=0+γ0bσ0b⋅1-2γ0bΛ1-2γ0aΛ.
In this expression dχb/dΓ|Γ=0 is the slope of
the reference tracer (b) with respect to the age of air Γ at the
tropopause; Λ is the width of the atmospheric age spectrum; and
γ0 and σ0 are, respectively, the effective linear growth
rate and the VMR of the tracers at the tropopause. According to
, γ0 can be calculated as
γ0=c-2Λd,
in which c and d are time-dependent coefficients. In each month (t) they
are obtained by fitting a 5-year-prior time series of monthly VMR averages
of the considered tracer at the tropopause level (χ0(t′)) with the
following function:
χ0(t′)=χ0(t)[1+c(t′-t)+d(t′-t)2].
To derive lifetime estimates, as suggested in , we considered
only the latitudes in the so-called surf zone , between
30–70∘ N and 30–70∘ S. The
tropical regions are not suitable for estimating the stratospheric lifetime
using the tracer–tracer method due to the intense large-scale upwelling
. Similarly, the polar regions are not suitable for this study
due to the intense subsidence, especially during winter . For
each month of the MIPAS mission and each 5∘
latitudinal band between
30–70∘ N and 30–70∘ S, we determine the pressure level
corresponding to the tropopause as the level with a minimum in the monthly
average temperature profile. For CFC-11 we assume a lifetime τb= 52 (43–67) years . To determine the coefficients c and d
appearing in Eq. (), at each MIPAS measurement month t we fit a
time series of HATS (http://www.esrl.noaa.gov/gmd/hats/) CCl4
and CFC-11 global monthly averages. Each time series extends back in time for
5 years, starting from the month t. The calculation is then repeated for
each month of the MIPAS mission, from July 2002 to April 2012. For the
estimation of lifetimes limited to NH and SH we used, respectively, NH and SH
HATS monthly means instead of global monthly mean. We then used the
coefficients c and d to calculate the effective linear growth rate
γ0 via Eq. (), assuming Λ= 1.25 years as suggested in
and in .
To estimate the slope of CFC-11 with respect to the age of air at the
tropopause we used an analysis of air samples acquired onboard the
Geophysica aircraft . The analysis produces a
dχb/dΓ|Γ=0 value of -20.6± 4.6 ppt yr-1
for 2010. We calculated the slope for other years by scaling the 2010 value
according the relative change in the yearly γ0 average. For
Eq. () we used an average of the γ0 values obtained in the
whole MIPAS mission period.
We determined the slope of the correlation at the tropopause
dχa/dχb|tropopause according to the method suggested
by . We considered only the VMR monthly means of CFC-11 and
CCl4 at the SPARC pressure levels (see Sect. )
above the tropopause. First of all, the mean correlation curve has been
created calculating the mean of the CCl4 data within 2 pptv of CFC-11
wide windows. The slope of the data has been calculated using a linear least-squared fit within a moving window of 80 pptv of CFC-11. After the
calculation, the moving window would be shifted forward by 5 pptv and the
slope would be calculated again. The procedure was repeated for each
5∘ latitudinal band. As suggested in only CFC-11 VMRs
greater than 120 pptv are considered. This approach makes us confident that
the calculated slope is not affected by VMR values arising from the upper
stratosphere. The remaining data were fitted using a second-degree polynomial
to calculate the value of the slope at the tropopause.
We calculated the VMR at the tropopause (σ0) by averaging all the VMR
monthly averages at the tropopause pressure level. The monthly means are then
weighted using the corresponding atmospheric pressure. The atmospheric VMR
(σ‾) is calculated averaging the VMR monthly averages
weighted with atmospheric pressure, in the pressure range between 200 and
20 hPa. The calculation of σ0 and σ‾ of CCl4
and CFC-11 is carried out separately for each latitudinal band, yielding a
CCl4 global average lifetime of 47 (39–61) years, a lifetime of
49 (40–63) years in the NH, and 46 (38–60) years in the SH. We calculated
the CCl4 lifetime confidence interval by mapping the CFC-11 lifetime confidence interval through the
calculations (see , for more details). We also evaluated the impact of other possible
error sources using a perturbative approach. We found that a 10% bias in
the CCl4 VMR retrieved from MIPAS (see Sect. )
would cause an error of the order of 3–4 % in the CCl4 lifetime. An
uncertainty of ±4.6 ppt yr-1 in
dχb/dΓ|Γ=0 would cause an error smaller than
4% in the CCl4 lifetime. These contributions are by far smaller
than the error implied by the uncertainty in the CFC-11 lifetime.
Our CCl4 lifetime estimations are consistent with the most recent
literature that suggests an atmospheric lifetime of 44 (36–58) years
. Several older studies report atmospheric
CCl4 lifetimes between 30 and 50 years (Singh et al., 1976; Simmonds et al., 1988; Montzka et al., 1999; World Meteorological Organization (WMO), 1999; Allen et al., 2009). studied the stratospheric lifetime
of several species (including CFC-11 and CCl4) using ACE-FTS
measurements. Using a CFC-11 lifetime of 45 ± 7 (World Meteorological Organization (WMO), 2011) they
calculated a CCl4 global lifetime of 35 ± 11 years. The difference
with our results is explained taking into account the different reference
CFC-11 lifetimes used: using the same CFC-11 lifetime (World Meteorological Organization (WMO), 2011) we would
obtain a CCl4 lifetime of 41 ± 6 years. also report
very different lifetimes in the two hemispheres (41 ± 9 years in the NH and
21 ± 6 years in the SH) but they are not able to provide a solid
justification for this finding. Again, the differences with our results are
partially explained with the different CFC-11 lifetime considered (using the
same CFC-11 lifetime (World Meteorological Organization (WMO), 2011) we would obtain a CCl4 lifetime of
42 ± 7 years in the NH and 40 ± 6 years in the SH) but the choice of
different reference lifetimes does not explain the hemispheric asymmetry
reported in .
Conclusions
The ESA version 7 processor has been used to determine for the
first time the CCl4 VMR global distribution in the UTLS using MIPAS
measurements. The MIPAS CCl4 observations cover the altitude range
from 6 to 27 km and, having been obtained from emission measurements, provide
a global coverage. The zonal means of CCl4 VMR show features typical
of long-lived species of anthropogenic origin that are destroyed primarily in
the stratosphere by photolysis. The highest VMR values are found in the
troposphere, and VMR monotonically decreases with increasing altitude in the
stratosphere. In the lower stratosphere, the largest values are observed
between 30∘ S and 30∘ N due to
the intense updraft that occurs in the tropical region. The CCl4
global distribution also shows a seasonal variability. This seasonality is
more evident in the polar regions due to CCl4-poor mesospheric air
subsidence induced by the polar vortex.
We calculated interhemispheric VMR differences in the UTLS as a function of
pressure and latitude using MIPAS average CCl4 profiles. At high
latitudes, the asymmetry likely stems from the fact that the polar vortex in
the Antarctic is systematically stronger, more stable, and of longer duration
than the Arctic polar vortex. At midlatitudes, NH and SH seasons are more
symmetrical and the CCl4 mean differences between the two hemispheres
are probably caused by the larger CCl4 emissions in the NH
. The weighted mean of NH-SH CCl4 differences
in the lowermost pressure levels sounded by MIPAS is consistent with the IHG
value reported by .
We compared MIPAS CCl4 profiles to profiles derived from the balloon
version of MIPAS and from the solar occultation ACE-FTS instrument.
While the MIPAS-B intercomparison covers both FR and OR mission phases at
selected latitudes, the ACE intercomparison covers the OR phase, globally, for
latitudes larger than 45∘. In general, MIPAS/ENVISAT measurements are
within 10 % of both instruments for pressures between 100 and 40 hPa. A
positive bias is found mainly in tropical regions at very low altitudes for
OR measurements. In the latitude band
50–70∘ S, MIPAS shows a larger
negative bias with respect to ACE-FTS, but this bias seems to reduce when
compared with the upcoming version of ACE-FTS products. For pressures smaller
than 40 hPa, MIPAS/ENVISAT CCl4 values are between MIPAS-B and
ACE-FTS.
We used the CCl4 measurements to estimate for the first time the
CCl4 trends as a function of both latitude and pressure, including
the photolytic loss region (70–20 hPa). Negative trends
(-10 to -15 pptv decade-1, -10 to -30 % decade-1) are observed at all latitudes in
the UTLS region, with the exception of slightly positive values
(5–10 pptv decade-1, 15–20 % decade-1) for a limited region at Southern
midlatitudes between 50 and 10 hPa. We attribute positive stratospheric
trend to the less effective mixing mechanisms in the stratosphere as compared
to the troposphere at these latitudes. In general, CCl4 VMR values
exhibit a smaller decline rate for the SH than the NH. The magnitude of the
negative trend increases with altitude, more strongly in the NH, reaching
values of 30–35 % decade-1 at 50 hPa, close to the lifetime limited rate. The
hemispheric asymmetry of the trend is probably related to the asymmetry in
the general circulation of the atmosphere.
An approach based on tracer-tracer linear correlations was used to estimate
CCl4 atmospheric lifetime in the lower stratosphere. The calculation
provides a global average lifetime of 47 (39–61) years considering CFC-11 as
a reference tracer. These results are consistent with the most recent
literature results of 44 (36–58) years . We also
computed the CCl4 lifetime separately for the two hemispheres,
obtaining 49 (40–63) years for the NH and 46 (38–60) years for the SH.
MIPAS ESA Level 2 products version 7 can be obtained via
https://earth.esa.int/web/guest/data-access (registration required). Trend
values and related errors used to build the maps of Fig.
are available upon request to the authors.
The authors declare that they have no conflict of interest.
Acknowledgements
MIPAS ESA v7 data were processed in the frame of contract no. 21719/08/I-OL
funded by ESA. We thank AGAGE leaders Ronald G. Prinn, Ray F. Weiss, Paul Krummel, and Simon O'Doherty for providing AGAGE data. AGAGE is supported principally by NASA
(USA) grants to MIT and the Scripps Institution of Oceanography, by DECC
(UK) and NOAA (USA) grants to Bristol University, and by CSIRO and BoM
(Australia). We thank the NOAA Climate Program Office for the support and for the
NOAA/ESRL/HATS CCl4 data. Funding for the Atmospheric Chemistry
Experiment was supplied primarily by the Canadian Space Agency.
Edited by: Martin Dameris
Reviewed by: four anonymous referees
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