We present new observations of trace gases in the stratosphere
based on a cost-effective sampling technique that can access much higher
altitudes than aircraft. The further development of this method now provides
detection of species with abundances in the parts per trillion (ppt) range
and below. We obtain mixing ratios for six gases (CFC-11, CFC-12, HCFC-22,
H-1211, H-1301, and SF6), all of which are important for understanding
stratospheric ozone depletion and circulation. After demonstrating the
quality of the data through comparisons with ground-based records and
aircraft-based observations, we combine them with the latter to demonstrate
its potential. We first compare the data with results from a global model driven
by three widely used meteorological reanalyses. Secondly, we focus on CFC-11
as recent evidence has indicated renewed atmospheric emissions of that
species relevant on a global scale. Because the stratosphere represents the
main sink region for CFC-11, potential changes in stratospheric circulation
and troposphere–stratosphere exchange fluxes have been identified as the
largest source of uncertainty for the accurate quantification of such
emissions. Our observations span over a decade (up until 2018) and therefore
cover the period of the slowdown of CFC-11 global mixing ratio decreases
measured at the Earth's surface. The spatial and temporal coverage of the
observations is insufficient for a global quantitative analysis, but we do
find some trends that are in contrast with expectations, indicating that the
stratosphere may have contributed to the slower concentration decline in
recent years. Further investigating the reanalysis-driven model data, we find
that the dynamical changes in the stratosphere required to explain the
apparent change in tropospheric CFC-11 emissions after 2013 are possible
but with a very high uncertainty range. This is partly caused by the high
variability of mass flux from the stratosphere to the troposphere,
especially at timescales of a few years, and partly by large differences
between runs driven by different reanalysis products, none of which agree
with our observations well enough for such a quantitative analysis.
Introduction
Many halogenated trace gases are strong greenhouse gases and/or are involved
in the ongoing depletion of the ozone layer; therefore, observations of these
in the stratosphere are valuable. Moreover, measurements of some of these
species allow us to constrain changes in stratospheric circulation and
transport across the tropopause. An analytical challenge is posed by the low
abundances of many such gases, in combination with the low ambient pressures
found in this region of the atmosphere. Another challenge is the ability to
reach the stratosphere as even the highest-flying research aircraft can only
reach altitudes just above 20 km (Schauffler et al., 2003; von Hobe et al.,
2013). This is modest considering that the stratosphere extends to around 50 km. Large high-altitude balloons can reach altitudes of up to about 36 km
(Engel et al., 2009; Ray et al., 2017), but due to the heavy payloads, they are
increasingly difficult to fly given the risks for people living in landing
areas and the cost or risk from lifting gases such as helium or hydrogen.
Satellite (or aircraft) remote sensing plays an important role and can offer
a global picture for some gases (Stiller et al., 2008; Santee et al., 2013;
Harrison et al., 2019), but measurement precision and altitude resolution
are often limited. They are also indirect observations and require continued
validation through independent in situ methods. Generally, the mentioned platforms
are rather expensive, ranging from costs of the order of EUR 10 000
per flight hour for aircraft to satellite costs of millions of euros. The
relatively recently developed AirCore technique (Karion et al., 2010), with
flight costs of below EUR 2000 (depending on the setup), offers a
cost-effective alternative. AirCores, which were named due to similarities
to ice cores extracted from glaciers, are based on the concept of flying a
very long lightweight coiled piece of stainless-steel tubing on a weather
balloon. The tube is open on one end and therefore empties naturally upon
ascent as ambient pressures decrease. During descent a full vertical profile
of air is collected between the balloon's burst altitude (up to 36 km) and
ground level. This technology has been demonstrated before but for
providing measurements of only a handful of higher abundance trace gases
such as CO2 and CH4 (Karion et al., 2010; Membrive et al., 2017;
Engel et al., 2017) and their isotopic composition (Mrozek et al., 2016;
Paul et al., 2016).
However, due to the limited amount of air that is captured by AirCores, no
ozone-depleting substances (ODSs) have been investigated yet, as their
abundances are well below 1 ppb (parts per billion). The importance of such
observations is, however, demonstrated by the following example. The recent
work by Montzka et al. (2018) on renewed emissions of CFC-11 has received
much attention since it indicates a substantial and ongoing breach of the
global treaty designed to prevent the destruction of the stratospheric ozone
layer: the Montreal Protocol on Substances that Deplete the Ozone Layer.
According to their study, global CFC-11 emissions increased by 13±5 Gg yr-1 when comparing periods before and after 2012 with the caveat
that up to 50 % of that derived emission change might actually be
attributable to changes in stratospheric processes or dynamics. More
recently, Rigby et al. (2019) found similar global increases of 11–17 Gg yr-1 over 2014–2017 vs. the 2008–2012 average, and they also pinpointed a
concurrent emissions increase source of 7.0±3.0 Gg yr-1 to
eastern mainland China. However, they found no emission increases in other
parts of the world covered by regular ground-based observations. This could
mean that some of these emission increases have arisen in regions where no
such measurements are available. An alternative explanation, i.e. the
possibility of a sustained change to the amount of CFC-11 exchanged between
the troposphere and the stratosphere as the driving mechanism for at least a
part of the anomaly, has, however, not been ruled out so far.
Aircraft- and balloon-based mixing ratios of six halogenated trace
gases in the upper troposphere and stratosphere as compared to the NOAA/GMD
ground-based northern hemispheric GGGRN time series
(https://www.esrl.noaa.gov/gmd/, last access: 11 January 2020). HCFC-22 has a significant sink process in
the troposphere and therefore exhibits stronger inner-hemispheric gradients.
To illustrate that, we compare the mid-latitude station at Mace
Head, Ireland, with the subtropical station at Mauna Loa, Hawaii. Lower mixing
ratios generally represent higher altitudes. For all gases except SF6,
some higher-altitude data are not shown to better demonstrate the good
comparability of near-tropopause data to the NOAA time series. The complete
corresponding data including uncertainties can be found in the Supplement
(see also Figs. S1 to S4).
Methods
Dry air mole fractions of halogenated trace gases were derived from air
samples collected on board three different platforms: a passenger aircraft
(CARIBIC; Brenninkmeijer et al., 2007) flying at altitudes of 8–13 km
(11 flights, 2009–2016), a research aircraft (Leedham Elvidge et al., 2018)
accessing higher altitudes of 9–21 km (M55 Geophysika, five campaigns,
2009–2017), and the first measurements of such gases with the relatively
recently developed AirCore methodology (Karion et al., 2010; 8–30 km, 15 flights in Finland and the UK, 2016–2018). The aircraft data have partly
been published before (Leedham Elvidge et al., 2018; Laube et al., 2013).
The balloon-based AirCore technique was developed further, mainly through the
use of specially designed tubing that maximises the amounts of air collected
in the stratosphere, as well as through a novel subsampling technique that
minimises the use of contamination-prone materials. The amount of
retrievable stratospheric air, however, is still more than 2 orders of
magnitude smaller than from aircraft-based sampling techniques. With
laboratory analytical improvements compensating for this, the AirCore
measurements show good precisions (ranging from 0.2 % to 3.3 % compared
with 0.4 % to 1.1 % for aircraft samples) and excellent agreement with the
aircraft data. The other important challenge for AirCore measurements of
halocarbons is to ensure that the air is not contaminated throughout the
entire sampling and subsampling process. Contaminations can arise from
leakages and/or halocarbon-emitting materials (such as organic polymers) in
the AirCore itself, in the CO2 analyser system including the pump, or
in the subsampling system. Importantly, for all compounds reported here,
mixing ratios in the stratosphere are much lower than in even remote
tropospheric regions, let alone near sources of these gases. In addition,
almost all of the contamination possibilities would affect the entire
profile as an AirCore is essentially one air sample. This would become
apparent in the correlations of the species with each other, which are very
compact in the stratosphere. In the absence of such correlation breakdowns
(see Figs. 1, 2, and S1 to S4), we therefore conclude that such
contaminations are at undetectable levels in the dataset presented here.
More details can be found in Table 1 and the Supplement.
All samples were processed with a previously described analytical system and
methodology (Laube et al., 2010, 2012) using cryogenic extraction and
pre-concentration, followed by gas chromatographic separation and detection
with a high-sensitivity mass spectrometer. Trace gas measurements from this
system as well as mean ages of air (AoAs, i.e. average stratospheric transit
times; see section 3.1 for more details) calculated from these have been
shown to compare very well with those of other internationally recognised
measurements over several decades (Leedham Elvidge et al., 2018; Laube et
al., 2013; Trudinger et al., 2016).
Stratospheric trends at AoA surfaces were derived by fitting second- and
third-order polynomials (depending on whether an inflexion point was
observed) to the respective correlations of mixing ratios and AoAs. The
formulas of the polynomials were then used to interpolate onto the AoA
surfaces (1, 2, 3, and/or 4 years, depending on which AoA range was covered)
for each flight. To test the uncertainty of this method, the data for each
flight were first replicated four times, where each replicate was
modified by plus or minus the uncertainty in the mixing ratio and mean age
uncertainties. This resulted, for each data point, in the average plus
minimum and maximum value for both mixing ratio and AoA. Subsequently, 5n (n
being the number of data points available for each flight) random samples
were drawn (repeat draws possible) with a bootstrap algorithm (as in Volk et
al., 1997; Laube et al., 2013), and a second- or third-order polynomial
again fitted. This procedure was repeated 500 times for each flight,
resulting in an average mixing ratio and an uncertainty range at each AoA
surface. The derived mixing ratios were subsequently used to produce linear
regressions over time, including a weighting by the inverse uncertainties of
the individual CFC (chlorofluorocarbon) mixing ratios. The bootstrapping algorithm (500 repeat
draws, repeat draws possible) was used again to ensure that the derived
slope uncertainties were not underestimated and that individual high or low
points did not bias the slope estimates.
Observation-based data were compared to model output from the Chemical
Lagrangian Model of the Stratosphere (CLaMS), a Lagrangian chemical
transport model with advective transport calculated from three-dimensional
forward trajectories and an additional parameterisation for small-scale
turbulent mixing (McKenna et al., 2002). Potential temperature is used as
vertical coordinate throughout the stratosphere with vertical velocity
estimated from the total diabatic heating rate. Further model details and
the chemistry scheme used are described in Pommrich et al. (2014). For the
simulations used in this study, CLaMS was driven with horizontal winds and
diabatic heating rates from three alternative meteorological reanalysis datasets: ERA-Interim (from the European Centre for Medium-Range Weather Forecasts,
ECMWF), JRA-55 (from the Japan Meteorological Agency), and MERRA-2 (from NASA).
For more information on methods, calibrations, and modelling, as well as
additional data, please see the Supplement.
Results and discussionObservational data overview and comparisons
Our data are based on measurements of air samples collected in the upper
troposphere and stratosphere of the northern hemisphere using aircraft and
weather balloons between 2009 and 2018. Figure 1 shows the obtained mixing
ratios alongside the northern hemispheric “background” time series derived
through the combination of observations at various ground-based stations
within the National Oceanic and Atmospheric Administration Global Monitoring
Division's Global Greenhouse Gas Reference Network (NOAA/GMD GGGRN). It is
apparent that both the aircraft and the balloon data follow the ground-based
trends quite well for all six gases. Slightly enhanced mixing ratios can
often be observed in the vicinity of the tropopause (see also Figs. S5 and
S6), mostly due to recent influences from regional emissions (Kloss et al.,
2014; Leedham Elvidge et al., 2015; Oram et al., 2017). This is especially
pronounced in the research aircraft data from 2017, which belong to a
campaign (Höpfner et al., 2019) exploring the atmospheric composition
above the polluted Asian monsoon region (Randel et al., 2010; Vogel et al.,
2019). It is, however, worth noting that most species' enhancements are not
significantly higher than the combined measurement uncertainties, which
demonstrates the importance of the consistency of the datasets and
therefore the quality of the stratospheric record. Figure 1 also illustrates
the much improved temporal density that AirCore observations have provided
from 2016 onwards (in comparison to aircraft campaigns), especially at
altitudes above 15 km, which are out of the reach of all but a few research
aircraft.
Stratospheric CFC-12 mixing ratios and the mean age of air (AoA)
as a function of CFC-11 mixing ratios, as observed in air samples collected
by research aircraft (diamonds) and AirCore samples (circles). Crosses denote the
values obtained from the CLaMS model sampled at the same times and
coordinates as the observations but, for better visibility, only from 2016
onwards. The CLaMS model was run using three different meteorological
reanalysis packages: ERA-Interim (black), JRA-55 (blue), and MERRA-2 (red).
Panel (a) shows a comparison of observation-based CFC-11
mixing ratio trends at mean ages of air of 2 (blue), and 4 (red) years
with those from the CLaMS model run driven by the ERA-Interim reanalysis
(grey and yellow) in the northern hemispheric stratosphere. The latter have
been derived as averages between 30 and 90∘ N. The dashed and
dotted lines correspond to regression lines (weighted by their 1σ
standard error for observations) and an illustration of their 2σ
uncertainties over the time periods displayed. Panel (b) shows the
same comparison but at mean ages of 1 and 3 years. The numerical
values can be found in Table 2.
In the stratosphere, trace gases typically exhibit compact interspecies
correlations (Schauffler et al., 2003; Volk et al., 1997), and some gases
(such as SF6) can be utilised to derive average stratospheric transit
times, which are more commonly known as mean ages of air (AoAs; Engel
et al., 2009; Ray et al., 2017; Stiller et al., 2008; Leedham Elvidge et
al., 2018). The correlations between CFC-11 and CFC-12 as well as between
CFC-11 and AoA derived from observations (see Supplement Sect. S1.2 for details) are shown in
Fig. 2. Two things are apparent. Firstly, this again demonstrates the
consistency and quality of our data as similar correlations are observed for
both aircraft- and AirCore-based mixing ratios over the entire range.
Secondly, the correlations have not undergone a large shift in the last 10 years. Correlations between trace gases are often driven by changes in
tropospheric trends, as tropospheric air keeps “feeding” the stratosphere.
A large shift in these correlations would therefore not be expected as both
CFC-11 and CFC-12 have experienced relatively small negative tropospheric
trends in recent years (Montzka et al., 2018; Rigby et al., 2019). However,
there are other factors that can change the correlations, namely changes in
stratospheric chemistry and transport. The CFC-11–AoA correlation in
particular would be affected if, for example, the main transport pathways and or
times (AoAs) inside the stratosphere had changed. This possibility is
investigated further below.
Comparisons with model data using different reanalyses
We first focus on a comparison of model simulations with the aircraft and
AirCore data. Also shown in Fig. 2 are data from simulations with the
Chemical Lagrangian Model of the Stratosphere (CLaMS; McKenna et al., 2002;
Pommrich et al., 2014). The latter was driven alternatively by three
commonly used meteorological reanalyses, i.e. ERA-Interim, JRA-55, and
MERRA-2 (Dee et al., 2011; Kobayashi et al., 2015; Gelaro et al., 2017).
These newest available meteorological reanalysis datasets provide the best
guess of the current state of the atmosphere. We use the differences between
them to quantify the uncertainty in our knowledge of the stratospheric
circulation and its changes. The model was sampled at coordinates and times
coinciding with those of the observations. Results from all three runs are
similar to those from observations in the case of the correlation of CFC-11
with CFC-12. The CFC-11–AoA correlation in Fig. 2 is a measure of the
speed of the main stratospheric overturning circulation as it reflects, in
an integrated way, the speed and pathway of trace gas transport through the
stratosphere. Here, the model data for both ERA-Interim and JRA-55 remain
close to the observed values throughout the range. The MERRA-2-based data
does, however, stand out producing higher AoAs at similar stratospheric CFC-11
mixing ratios and an increasing discrepancy with increasing AoA. As noted by
Ploeger et al. (2019), the MERRA-2 reanalysis has a slower stratospheric
circulation, and our observational evidence strongly indicates that it is
indeed too slow. This is a consistent feature, which is also apparent when
comparing with MERRA-2-based data from before 2016 (not shown in Fig. 2).
The details of the causing mechanisms could be complex and are beyond the
scope of this work.
Comparison of average measurement uncertainties (derived as the
average of 1 standard deviation from repeated working standard or air sample
measurements) of the research aircraft campaign in 2016; all AirCore
flights; and some AirCore sample repeats for CFC-11, CFC-12, H-1211, H-1301,
HCFC-22, and SF6. For AirCore uncertainties, the average working
standard uncertainty over 3 years was used as it is (a) more
representative of the entire measurement period and (b) generally comparable
or worse than precisions derived from sample repeats. AirCore-based
precisions are generally slightly worse than those achieved with the larger
aircraft-based samples but still much smaller than mixing ratio gradients
observed in the stratosphere.
Trace gasAverage precision (%)Average precision (%)Average precision (%)of aircraft 2016 measurementsof AirCore 2016–2018of AirCore 2017standardssample repeatsCFC-11 (CFCl3)0.40.91.2CFC-12 (CF2Cl2)1.11.20.8H-1211 (CF2ClBr)0.61.91.0H-1301 (CF3Br)0.63.32.3HCFC-22 (CHF2Cl)0.60.90.2SF60.40.90.6Long-term trends of trace gases in the stratosphere
Focusing on the details of the correlations in Fig. 2, we investigate
whether there are indications here that might partly explain the recent
changes in the tropospheric trend of CFC-11. Most air enters the
stratosphere in the tropics and is then transported poleward. CFC-11 and
CFC-12 molecules are mostly destroyed in the tropical stratosphere (Douglass
et al., 2008). Transport of the remainder of these gases to the poles is
much slower than in the troposphere and takes several years (Kida, 1983;
Schmidt and Khedim, 1991) as is reflected in the CFC-11–AoA correlation in
Fig. 2. In the case of an acceleration of parts of the circulation, for which
there have been observational indications (Bönisch et al., 2011; Stiller
et al., 2012), that correlation should therefore shift. We consequently
fitted the CFC-11–AoA correlation with a second- or third-order polynomial
for each individual research aircraft and balloon flight and calculated the
mixing ratio of CFC-11 after having spent, on average, 1, 2, 3, and
4 years in the stratosphere. Figure 3 shows examples of the trends at the
four residence times from 2009 to 2018, and the full data can be found in
the Supplement.
While there is substantial variability of mixing ratios at these AoA
surfaces over time, we do find a positive trend (increases from 3 % to 10 %) from 2009 to 2018 for all observation-based (aircraft and AirCore)
estimates. The trends at an AoA of 1 and 4 years are not significantly
positive, but the ones at 2 and 3 years are, within 2.0 and 1.6
standard deviations of the slope uncertainties, respectively (Fig. 3, Table 2). These stratospheric trends contrast the tropospheric trend of CFC-11,
which has been negative throughout that period (∼-6 % in
total, Fig. 1). While there is a certain lag time for air to reach our
stratospheric observation points (i.e. 1, 2, 3, and 4 years on average),
CFC-11 had been decreasing nearly linearly in the troposphere since the late
1990s. In turn this implies that changes in stratospheric circulation may
indeed have played a substantial role in the recent changes to the
tropospheric trend of CFC-11 as previously suspected (Montzka et al., 2018).
The causes are not explicable with an integrated quantity such as AoA as the
underlying distribution of stratospheric transit times cannot currently be
inferred from trace gas observations. However, it should be noted that the
limited temporal and spatial coverage of the observation-based measurements and
especially the gap between 2011 and 2016 represents an additional and unquantifiable source of uncertainty.
Temporal trends and their 2σ uncertainties of CFC-11,
CFC-12, HCFC-22, and H-1211 mixing ratios at AoAs of 1, 2, 3, and
4 years. These slopes correspond to an uncertainty-weighted regression
line fitted to the data in Figs. 3, and S8–S12, with two exceptions: (1) the data from the Asian monsoon campaign in 2017 was excluded as this region is not representative of northern hemispheric stratospheric air and (2) all data at mean
ages above 3.5 years from winter campaigns in high latitudes were also
excluded as they might contain polar vortex air, which is equally
unrepresentative. Model-based slopes were derived over the same period as
observational data (August 2009–August 2018), except for JRA-55 and MERRA-2, where
data were only available until the end of 2017.
For the other three gases that have sufficient measurement precisions for
such an analysis (i.e. CFC-12, H-1211, and HCFC-22), we also find a picture
that does not agree well with their tropospheric trends (Table 2). Both
CFC-12 and H-1211 have been in decline in the troposphere since the
mid-2000s and decreased by ∼6 % and ∼20 % between late 2009 and late 2018, respectively (Fig. 1), whereas
tropospheric HCFC-22 mixing ratios have increased monotonically (and by
∼25 % during our observation period) since the trace gas appeared
in the atmosphere several decades ago, albeit with a recent slowdown. In
contrast, in the stratosphere, we find that CFC-12 decreased at all mean age
surfaces but not as much as in the troposphere (-0.9 % to -4 %); HCFC-22
increased disproportionally by 30 % to 38 %; and H-1211 decreased, but only
at a mean age of 1 year (-9 %). No significant change occurred at
2 years, and 9 % to 22 % increases were observed at 3 and 4 years. For the
latter three gases, this unexpected behaviour could be partly related to
changes in tropospheric trends in the period leading up to 2009, as a
significant part of the air at certain mean age levels is much older than
the mean age itself. However, these effects should subside over the decade
that our observations span, especially for H-1211, which is the
shortest-lived gas of the four. In addition, CFC-11 should not be affected
as it has been decreasing for much longer. The underlying mechanisms are
likely complex.
The only straight-forward possibility to generate positive CFC-11 trends in
the stratosphere between 2009 and 2018 would be an increase in the air
fractions that have younger and older residence times than the inferred mean
age. Such a 2-fold increase would maintain the same AoA, but would
influence the mixing ratios observed at the AoA surfaces in different ways.
If the increased older air fraction had been in the stratosphere for long
enough, it would have already lost virtually all of its content of
shorter-lived gases (H-1211 and CFC-11). However, if this older air fraction
at the same time would be in an AoA range where the longer-lived gases
(CFC-12 and HCFC-22) are still present in significant amounts, then an
increase in its share should lead to a decrease in CFC-12 and HCFC-22 mixing
ratios (but less so for the latter as it is much longer lived in the
stratosphere). To balance this increase in the older air fraction and
maintain a constant mean age, the younger fraction of the AoA spectrum would
also need to have an increased share. Younger air generally contains higher
mixing ratios of all four gases – and disproportionally so for HCFC-22 as
its tropospheric mixing ratios continue to increase. If the increases in the
two fractions of the AoA spectrum would be in the right AoA range, the
overall effect would then be an increase of mixing ratios of CFC-11, H-1211,
and HCFC-22 over time at a given AoA surface, accompanied by a decrease in
CFC-12 mixing ratios. This would then be entirely consistent with the
changes we observed at almost all AoA levels between 2009 and 2018.
Therefore, such a change to the stratospheric transit time distributions
could be considered the simplest case that would qualitatively explain
our observations.
The aforementioned possibility to at least partly explain such trends could
include an acceleration of air mass transport through the lower tropical
stratosphere (i.e. below the main sink region of CFC-11) as, for example,
CLaMS–ERA-Interim qualitatively shows over the relevant period (Fig. S15).
However, when compared with ERA-Interim-based model data at the same
transport times (Fig. 3), the model results show a different CFC-11 trend
in the lower stratosphere. In fact, the model- and observation-based trends
at mean ages of 1 and 2 years do not agree within 2 standard
deviations. This discrepancy is likely related to a known problem with
ERA-Interim, which generally overestimates the speed of the circulation in
that lower stratospheric region (Dee et al., 2011; Ploeger et al., 2012). At
larger mean ages, we find better agreement between the observations and the
model with the model data even reproducing the observed insignificant trend.
JRA-55-based model trends are very similar to those from the
ERA-Interim-based analysis, whereas the MERRA-2 reanalysis shows larger
differences to observations, both in terms of mixing ratios and trends
(Table 2, Figs. 3, and S8–S12). The generally limited comparability of
model and observations sheds some light on the ability of current reanalysis
products to quantify structural changes in stratospheric circulation
patterns.
Mass flux estimates of CFC-11
Nevertheless, we use the reanalysis-driven model data as the best available
means to derive the downward mass flux of CFC-11 through the extratropical
tropopause, i.e. the quantity describing how much CFC-11 is transported back
to the troposphere. Comparing the three simulations driven with three
different reanalyses provides an estimate of uncertainty due to
representations of stratospheric circulation changes. A temporal increase of
the stratosphere-to-troposphere mass flux could cause changes to the
tropospheric trend of CFC-11, which would look like renewed emissions. Such
a flux increase could be consistent with the observed increases in CFC-11
mixing ratios on AoA surfaces (Sect. 3.3) if accompanied by an increased
fraction of air entering the stratosphere without passing through the main
CFC-11 sink region in the lower tropical stratosphere (and instead entering, for example, through the Asian summer monsoon).
The NOAA/GMD tropospheric time series of CFC-11 serves as the boundary
condition for the model, and consequently in the absence of stratospheric
changes, the temporal trend of the mass flux should be similarly negative and
of a similar magnitude. The model generally reflects this reasoning over
longer time periods as can be seen in Fig. 4. We then follow the approach
by Montzka et al. (2018) to investigate whether the changes to the
tropospheric trend around 2013 might partly be caused by more CFC-11 being
transported back into the troposphere. For that purpose, we split the data
into two periods: before and after 2013. Independent of which definition of
the tropopause is being used (see the Supplement for details), we find an
increase in the mass flux of around 37 Gg yr-1 after 2013 for
CLaMS–ERA-Interim. This would explain 270 % of the observed slowdown of
CFC-11 mixing ratio decreases after 2013 when comparing to the 13±5 Gg yr-1 emission increase inferred by Montzka et al. (2018). At first
glance, this very high stratospheric contribution is not consistent with the
findings of both Montzka et al. (2018) and Rigby et al. (2019), who estimated
40 % to 60 % of the slowdown to belong to renewed emissions. However, the
global stratosphere-to-troposphere mass flux is very large compared to the
amount of unexplained emissions, and a direct quantitative comparison is not
possible, as explained in the following. When repeating the same model run,
but with an artificial tropospheric CFC-11 trend that continues to decrease
linearly after 2013 the mass flux remains very similar to the reference
simulation (difference of <0.6 Gg yr-1). This translates into a
minor influence of recent tropospheric trend changes on these stratospheric
fluxes and therefore confirming that this signal is indeed driven by
stratospheric changes in the ERA-Interim world. However, this pronounced
turnaround in 2013 is not a consistent feature for all three reanalyses, as
the JRA-55 run, despite producing such a similar picture in the correlation
comparisons (Fig. 2), in fact shows a further decrease of 0.4 Gg yr-1
(equivalent to -3 % of the new emissions signal) after 2013. The main
reason for that discrepancy is that, as opposed to ERA-Interim, JRA-55 does
not show a substantial change to the mass flux around 2013. Coming back to
the pre- and post-2013 analysis, CLaMS–MERRA-2 results are in between the
other two with 18.2 Gg yr-1 (135 %), but have the least credibility as
demonstrated by the poor comparability with observations. The main issue
connected with such an analysis is illustrated in Fig. 4. With annual
changes of up to 21 %, the variability of the CFC-11 mass flux from the
stratosphere to the troposphere is an order of magnitude higher than the 2013 change of 2 % to 5 % that we are trying to quantify. Some of that mass
flux variability occurs over several years, which severely limits the
capability of quantitatively determining trend changes between an 11-
and a 5-year period. It should, however, be re-emphasised that a mass flux
trend analysis over longer periods would be expected to work better and this
is indeed what we find for ERA-Interim and JRA-55. Between 2002 and 2017 the
CFC-11 flux from a linear regression of the model output driven by these two
reanalyses decreases by 10.5 % and 13.1 %, respectively, which is comparable
to the ∼11 % tropospheric decrease over the same period.
MERRA-2 again produces an outlier with only a 3.2 % decrease during those
16 years. The recent findings by Ray et al. (2020) of the QBO (Quasi-biennial Oscillation) significantly
modulating the variability of long-lived trace gases at the surface are
qualitatively consistent with our findings for both shorter and longer
periods. However, a quantification of this modulation is currently limited
by the uncertainties connected to the meteorological reanalyses in the
stratosphere. As shown in Fig. 4, the mass fluxes from the three
CLaMS-reanalysis runs show some covariation on QBO timescales but at the
same time also some significant differences which include offsets, long-term
trends, the magnitude of the variations, and the timing of changes.
The annually averaged stratosphere-to-troposphere mass flux of
CFC-11 through the tropopause between 2002 and 2018 for CLaMS model runs
driven by MERRA-2 (green), JRA-55 (blue) and ERA-Interim (black) reanalyses
including a linear regression for the period until 2013 (dashed). The red
line originates from an ERA-Interim sensitivity run for which tropospheric
CFC-11 was forced to continue to decrease at the same rate as before 2013.
Shown in grey and on the right-hand y axis are the two corresponding time
series of tropospheric CFC-11 mixing ratios (i.e. the real one, solid, and
the one with the forced decrease, dashed). The annual average has been
calculated by applying a 12-month running mean to the time series.
Conclusions
To summarise, we present new observations of six halogenated trace gases in
the stratosphere obtained from applying a further-developed AirCore
technology. These observations are consistent with ground-based measurements
of the same species at remote locations. They compare well to aircraft-based
observations, have good precisions, and offer a viable low-cost method for
directly observing ozone-depleting gases and circulation tracers in the
stratosphere at enhanced temporal and spatial resolutions. The derived mixing
ratios and mean stratospheric residence times, both from aircraft and AirCore
data, enable the assessment of the performance of the three most modern
currently available meteorological reanalysis packages. The ERA-Interim- and
JRA-55-derived model data compare better, whereas the MERRA-2-based data
exhibit distinctly slower transport through most of the region covered
here.
From a further analysis of the observational data at certain stratospheric
transport times, we also find insignificant to positive trends (within 1
standard deviation) of CFC-11 mixing ratios in the lower stratosphere
between 2009 and 2018 ranging from 3 % to 10 %. This is surprising and in
contrast to expectation from the tropospheric abundances, which have been
decreasing by about 6 % over that period. Similarly derived trends for
CFC-12, HCFC-22, and H-1211 are also not in good agreement with their
corresponding tropospheric trends. In a qualitative sense, and keeping in
mind the regional nature of these measurements and the uncertainties related
to the calculation of stratospheric transport times, this would point
towards increasing mass fluxes of CFC-11 being transported back to the
troposphere. Our observations therefore do support the hypothesis of new
emissions being lower than expected from tropospheric trends alone. More
generally, there is evidence for a significant and time-dependent role of
the stratosphere in the modulation of tropospheric trends of trace gases.
However, any further quantification of the stratospheric part of the CFC-11
story is prevented firstly by the non-global and intermittent nature of
sufficiently precise observations as well as their limited comparability to
model or reanalysis results; secondly by the variability of the CFC-11
stratosphere-to-troposphere mass flux influenced by, for example, QBO, ENSO (El Niño–Southern Oscillation), volcanic
eruptions, and also stratospheric transport changes as indicated by the
observed trace gas trends on AoA surfaces; and thirdly by the large
differences between results from different current meteorological
reanalyses. The quality of the latter is currently the main
limitation to refining such calculations.
Finally, our observations span 10 years, which is a short time in
comparison to the long-term climate-change-driven stratospheric circulation
changes expected from global models, which are of the order of decades
(Polvani et al., 2018). Our data, however, demonstrate the capabilities of the
AirCore observations to increase data coverage and better constrain such
changes on various timescales.
Data availability
Observational data are included in the Supplement, and the CLaMS model data
may be requested from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-9771-2020-supplement.
Author contributions
JCL conducted the analysis of the overall dataset, participated in
several campaigns, carried out some of the measurements, and led the writing
of the article. ECLE, BB, HC, ESD, PH, RK, AJH, AL, SR, CS, MT, ET, and WTS contributed to the
design of the AirCore and subsampling equipment and the various balloon
campaigns with ECLE, ESD, and ET also involved in the
halocarbon measurements and data analysis. CAMB and DEO were
responsible for CARIBIC, and TR was responsible for the Geophysika aircraft
measurement, sampling equipment, and related discussions. SAM
provided NOAA northern hemispheric time series and useful respective insights, and JUG and FP led the modelling analysis. All authors contributed to the
writing process of the article and scientific discussions surrounding it.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was funded by the ERC project EXC3ITE and the UK Natural Environment Research Council. David E. Oram also received support from the National Centre for Atmospheric
Science. We gratefully acknowledge the computing time for the CLaMS
simulations granted on the supercomputer JURECA at Jülich Supercomputing
Centre (JSC) under the VSR project ID JICG11. We thank all who helped with the
balloon launches in Finland and the UK, the numerous NOAA station personnel
and site scientists for sample flask collection and measurement, and Michel
Bolder for collecting the Geophysika air samples; we also acknowledge the work of
the Geophysika aircraft team. Related funding came from the European Space Agency (ESA, PremierEx and
FRM4GHG projects), Forschungszentrum Jülich, the European Commission
(FP7 projects RECONCILE,
StratoClim, and H2020 project RINGO). We further thank
Paul Konopka for carrying out some of the CLaMS simulations used here,
Jörn Ungermann for help with code translations, and Rolf Müller for
useful discussions.
Financial support
This research has been supported by the European Research Council (grant no. EXC3ITE (678904)), the Natural Environment Research Council (grant nos. NE/I021918/1 and NE/L002582/1), the Helmholtz Association (grant no. VH-NG-1128), the European Commission (grant nos. StratoClim-603557-FP7-ENV-2013-two-stage and RECONCILE-226365-FP7-ENV-2008-1), and the Dutch Science Foundation (NWO) (grant no. 865.07.001).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.
Review statement
This paper was edited by Peter Haynes and reviewed by two anonymous referees.
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