ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-3369-2018Evaluation of stratospheric age of air from CF4, C2F6,
C3F8, CHF3, HFC-125, HFC-227ea and SF6; implications for
the calculations of halocarbon lifetimes, fractional release
factors and ozone depletion potentialsLeedham ElvidgeEmmae.leedham-elvidge@uea.ac.ukhttps://orcid.org/0000-0002-6993-1271BönischHaraldhttps://orcid.org/0000-0002-1004-0861BrenninkmeijerCarl A. M.EngelAndreashttps://orcid.org/0000-0003-0557-3935FraserPaul J.GallacherEileenLangenfeldsRayMühleJenshttps://orcid.org/0000-0001-9776-3642OramDavid E.RayEric A.RidleyAnna R.RöckmannThomashttps://orcid.org/0000-0002-6688-8968SturgesWilliam T.WeissRay F.https://orcid.org/0000-0001-9551-7739LaubeJohannes C.School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UKInstitute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, GermanyAtmospheric Chemistry Division, Max Planck Institute for Chemistry, Mainz, GermanyInstitute for Atmospheric and Environmental Sciences, Goethe University of Frankfurt, Frankfurt, GermanyClimate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Victoria, AustraliaScripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USAChemical Sciences Division, Earth Systems Research Laboratory, NOAA, Boulder, Colorado, USACooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USAInstitute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the NetherlandsEmma Leedham Elvidge (e.leedham-elvidge@uea.ac.uk)8March20181853369338510August201713September201718December20174January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/3369/2018/acp-18-3369-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/3369/2018/acp-18-3369-2018.pdf
In a changing climate,
potential stratospheric circulation changes require long-term monitoring.
Stratospheric trace gas measurements are often used as a proxy for
stratospheric circulation changes via the “mean age of air” values derived
from them. In this study, we investigated five potential age of air tracers
– the perfluorocarbons CF4, C2F6 and C3F8 and the
hydrofluorocarbons CHF3 (HFC-23) and HFC-125 – and compare them to the
traditional tracer SF6 and a (relatively) shorter-lived species,
HFC-227ea. A detailed uncertainty analysis was performed on mean ages derived
from these “new” tracers to allow us to confidently compare their efficacy
as age tracers to the existing tracer, SF6. Our results showed that
uncertainties associated with the mean age derived from these new age tracers
are similar to those derived from SF6, suggesting that these alternative
compounds are suitable in this respect for use as age tracers. Independent
verification of the suitability of these age tracers is provided by a
comparison between samples analysed at the University of East Anglia and the
Scripps Institution of Oceanography. All five tracers give younger mean ages
than SF6, a discrepancy that increases with increasing mean age. Our
findings qualitatively support recent work that suggests that the
stratospheric lifetime of SF6 is significantly less than the previous
estimate of 3200 years. The impact of these younger mean ages on three
policy-relevant parameters – stratospheric lifetimes, fractional release
factors (FRFs) and ozone depletion potentials – is investigated in
combination with a recently improved methodology to calculate FRFs. Updates
to previous estimations for these parameters are provided.
Introduction
The “mean age of air” (mean AoA), defined as the average time that an air
parcel has spent in the stratosphere, is an important derived quantity used
in several stratospheric research fields, often when direct physical or
chemical measurements are scarce, not available or inadequate. AoA is perhaps
best known for being a measure of the strength of the Brewer–Dobson
circulation (BDC) and as a means of determining air mass fluxes
between the troposphere and stratosphere (Bönisch et al., 2009). It is
also used in calculations to determine the state of recovery of the ozone
layer via its role in calculations of stratospheric lifetimes, ozone
depletion potentials (ODPs; Brown et al., 2013; Laube et al., 2013; Volk et
al., 1997) and effective equivalent stratospheric chlorine (Newman et al.,
2006).
Mean ages can be derived by comparing an observed abundance of a
stratospheric tracer to the tropospheric time series of that gas, assuming
that the trace gas in question is largely chemically inert in the
stratosphere and has a monotonically, ideally linearly, changing tropospheric
concentration (Hall and Plumb, 1994). Commonly used tracers include sulfur
hexafluoride (SF6) and carbon dioxide (CO2), which have been used
extensively to track large-scale stratospheric transport and transport trends
and to evaluate atmospheric residence times of ozone-depleting substances
(ODSs) and their impact on the ozone layer (Andrews et al., 2001; Engel et
al., 2002; Volk et al., 1997). There are, however, problems with using these
compounds as age tracers. The limitations of CO2 have been recently
outlined in detail by Engel et al. (2017) and include a complicated
tropospheric trend – in part due to the influence of its seasonal cycle
(Bönisch et al., 2009) – and a stratospheric CO2 source, i.e. the
oxidation of hydrocarbons. For SF6, recent research suggests its
lifetime has likely been overestimated, and thus it may be giving high-biased
mean ages. The evidence for a proposed reduction in SF6 lifetime comes
from both modelling and measurement studies, which have evaluated its
stratospheric loss mechanisms via electron attachment (most recently by
Kovács et al., 2017) and in the polar vortex (Andrews et al., 2001; Ray
et al., 2017). The most recent (at the time of writing) evaluation gives a
revised lifetime of 850 (580–1400) years (Ray et al., 2017). This is
considerably lower than the 3200-year lifetime used in the most recent
assessments of the Intergovernmental Panel on Climate Change (IPCC, 2013) and the
World Meteorological Organization (WMO, 2014). A revised
lifetime will impact the estimated global warming potential of SF6
(Kovács et al., 2017). These limitations do not preclude the use of
CO2 and SF6 as age tracers, but may require complex corrections or
limit the suitability of these gases to act as tracers in certain regions (Andrews et al., 2001;
Bönisch et al., 2009). With this study, we do not attempt to discredit
these extremely useful existing age tracers, but to add to the range of
available tracers to improve the overall understanding in this field.
As mentioned above, AoA is an important component in our understanding of
the BDC. The potential changes to the BDC as the troposphere warms are not
yet fully understood. Chemistry–climate models predict an increase in the
strength of the BDC (e.g. Li et al., 2008; Oberländer et al., 2013),
which would be observed as a negative trend in (or a move to younger) mean
ages. However, a time series of mean ages derived from stratospheric
observations of trace gases in the mid-latitudes above 25 km has not found a
significant trend over the past 40 years (Engel et al., 2009, 2017).
Stratospheric circulation is complex: the shallow and deep branches of the
BDC may be changing at different rates (Bönisch et al., 2011; Diallo et
al., 2012; Ray et al., 2014) and shorter-timescale dynamical changes driven
by the quasi-biennial oscillation or the El Niño–Southern Oscillation
may complicate or even mask long-term changes to the BDC (Mahieu et al.,
2014; Stiller et al., 2017). For this reason, if chemical tracers are to be
used to diagnose global changes to the BDC they must be chemically inert
throughout the stratosphere. Unfortunately, the influence of
SF6-depleted mesospheric air in the upper stratosphere (potential
temperature > 800 K) and the higher Southern Hemisphere
latitudes (poleward of 40∘ S) may bias SF6-derived mean ages in
these regions (Stiller et al., 2017).
The combination of the need for accurate age tracers to track
stratospheric circulation changes and the uncertainties surrounding existing
age tracers prompted us to investigate a suite of anthropogenic trace gases
with stratospheric lifetimes > 100 years to identify other
potential AoA tracers. Of particular interest are the alkane-derived
perfluorocarbons (PFCs), which are extremely long-lived, stable trace gases
(WMO, 2014), at least one of which, perfluoromethane (CF4), was
previously shown to have potential as an age tracer (Harnisch et al., 1999).
In this paper, we assess the use of six alternative stratospheric age
tracers
To enhance the readability of this paper we have
selected the most common name for each compound to use as its abbreviation,
even if this means mixing chemical conventions (e.g. CHF3 but
HFC-227ea). Full details for each compound are provided in Table 1.
:
CF4, perfluoroethane (C2F6), perfluoropropane
(C3F8), trifluoromethane (CHF3), pentafluoroethane (HFC-125)
and 1,1,1,2,3,3,3-heptafluoropropane (HFC-227ea) and compare them with the
existing age tracer SF6. An overview of all compounds discussed in this
paper, including current stratospheric lifetime estimates and
tropospheric growth rates, can be found in Table 1.
Overview of trace gases used in this study and their relevant
properties.
a Growth rates are annual values averaged from 2002–2012
and derived from our own records, apart from CF4, which is from the SIO
AGAGE CG time series 2004–2017 (Sect. 2), and SF6, for which higher-frequency 2004–2014 NOAA data are used (see Fig. S1 for
agreement between NOAA and UEA data). b Precision calculations are
outlined in Sect. 2. Here the precision is calculated only for the
tropospheric time series data. Stratospheric sample precisions are in Table 2.
c Total atmospheric lifetime. d Ray et al. (2017).
Supporting the potential use of “new” age tracers is the increasing number of
methods available for collecting stratospheric air samples. Recently, air from
the novel AirCore method has been used to calculate CO2-derived mean
ages (Engel et al., 2017) and lightweight stratospheric bag samplers have
also been developed (Hooghiem et al., 2017). These technologies provide
an excellent opportunity to increase the temporal and spatial coverage of
stratospheric measurements in an affordable manner. However, it is important
that the mean ages derived from these air samples (which may, in the case of
discrete air samples, be as little as 20 mL of air per sample) have a similar
level of uncertainty as more traditional samplers (i.e. large balloon-borne
cryosamplers and high-altitude research aircraft; Sect. 2), especially if we
wish to compare changes in mean ages over time. In Sect. 3 we provide details
of our uncertainty analysis to facilitate similar analyses on future mean age
calculations.
We investigated this set of tracers for a variety of reasons. Firstly, we
selected several tracers – CF4, C2F6, C3F8 and
CHF3 – with estimated stratospheric lifetimes greater than SF6
(Table 1) because of their potential to be suitably inert age tracers.
Secondly, we selected a tracer – HFC-227ea – with a lifetime shorter than
(the currently established) SF6 lifetime to provide a contrasting point of
comparison. Recently, the SF6 lifetime has been shown to be perhaps
closer to HFC-227ea than previously thought (Ray et al., 2017; Table 1) and
so we include it in our comparison. Finally, we included HFC-125 as a
potential age tracer as we believe its current estimated stratospheric
lifetime of 351 years (SPARC, 2013; derived from model outputs) is
potentially an underestimate based on preliminary mean age interpretations
at UEA (finalised data included throughout this paper). We believe the
lifetime of HFC-125 (C2, CHF2CF3) should fall between CHF3
(C1) and HFC-227ea (C3, CHF2CF2CF3). All seven of the
above-mentioned tracers currently fulfil the prerequisite of having
well-constrained monotonically increasing growth rates in the troposphere.
Methodology
Long-term tropospheric time series are required to define the input of each
tracer to the stratosphere. No definition of “long-term” has been set, but
several studies use a period of 10–15 years leading up to the stratospheric
measurement period as a suitable tropospheric time series input for mean age
calculations of 0–8 years or even up to 10 years if a time series at the
later end of this range is used (Engel et al., 2002, 2006; Haenel et al.,
2015). The University of East Anglia (UEA) has analysed whole air samples
from the Cape Grim Baseline Air Pollution Station in Tasmania, Australia
(https://agage.mit.edu/stations/cape-grim) since 1978 for all compounds
discussed in this paper except CF4. The Cape Grim (CG) air archive
contains trace gas records known to be representative of unpolluted Southern
Hemispheric air and so provides excellent records of globally-relevant
tropospheric growth rates (O'Doherty et al., 2014, and
references within).
UEA trace gas analysis of the CG
air archive has been well documented in previous publications, for example Fraser et
al. (1999) and Laube et al. (2013). Briefly, analysis is performed using a manual
cryogenic extraction and pre-concentration system built in-house and
connected to an Agilent 6890 gas chromatograph and a high-sensitivity
tri-sector mass spectrometer. Full details of the analytical system can be
found in Laube et al. (2010a, 2016). Of note is the instrument change
detailed in Laube et al. (2016), whereby C2F6 precision is improved
by analysing samples on a KCl-passivated Al PLOT column, alongside
measurements of SF6, C3F8, CHF3, HFC-125, and HFC-227ea
with an Agilent GS GasPro column. Prior to 2006, analysis was performed on a
previous version of the analytical system (still using a GasPro column) that
also used different air standards. Data analysed on this older instrument
were incorporated into the time series using standard intercomparisons and
standard-to-sample ratio comparisons and showed no significant differences.
The ions used to quantify the gases measured at UEA were C2F5+
(m/z 118.99) for C2F6, SF5+ (m/z 126.96) for SF6,
C3F7+ (m/z 168.99) for C3F8, CHF2+
(m/z 51.00) for CHF3, C2HF5+ (m/z 101.00) for HFC-125 and
C3HF7+ (m/z 151.00) for HFC-227ea.
These measurements have been published either as time series or as
comparisons to other long-term datasets for SF6 (Laube et al., 2013),
C2F6 (Trudinger et al., 2016), C3F8 (Trudinger et al.,
2016; Ray et al., 2017), CHF3 (Oram et al., 1998) and HFC-227ea (Laube
et al., 2010a; Ray et al., 2017). UEA HFC-125 has not been published
previously, but the UEA data agree very well with the CG observations made by
AGAGE (Advanced Global Atmospheric Gases Experiment; see website link above; data not
shown). Data from high-frequency in situ and archived CG air samples measured
by the Scripps Institution of Oceanography (SIO) and the AGAGE network have
also been provided for CF4, C2F6 and SF6. These samples
were analysed on a Medusa gas-chromatographic system with cryogenic
pre-concentration and mass spectrometric detection (Arnold et al., 2012;
Miller et al., 2008). SIO CG CF4 and C2F6 time series have
previously been published in Mühle et al. (2010) and Trudinger et
al. (2016) and their SF6 time series in Rigby et al. (2010). SIO
CF4 and SF6 data are reported on the SIO-05 scale and
C2F6 on the SIO-07 scale (Mühle et al., 2010; Prinn et al.,
2000).
To ensure the suitability of the CG measurements as a record of stratospheric
inputs we first compensated for the time lag between observed concentrations
in the Southern Hemisphere and the tropical upper troposphere – the main
stratospheric input region – by applying a 6-month time shift to all CG
records. The efficacy of this treatment was verified by comparing the offset
CG trends to tropical (20∘ N to 20∘ S) mid- to
upper-tropospheric aircraft data obtained from interhemispheric flights by
the CARIBIC
CARIBIC (Civil Aircraft for the Regular Investigation of
the atmosphere Based on an Instrument Container), part of IAGOS
(www.iagos.org), is an observatory based on approximately monthly
flights on-board a commercial Lufthansa Airbus A340-600 from Frankfurt to
destinations on several continents. Further details can be found at
http://www.caribic-atmospheric.com/.
observatory (Fig. 1). As can be
seen in Fig. 1, there are some gaps in the UEA CG time series. To smooth the
temporal distribution a polynomial fit was applied to each dataset and the
equation from this fifth-order (CHF3, HFC-125, HFC-227ea) or sixth-order
(SF6, C2F6 or C3F8) polynomial fit was used to
interpolate monthly mixing ratio values. The fit was applied to the central
section of each time series only (see Fig. 1) to avoid periods with
significantly different growth rates; for example, there was no significant
growth for HFC-125 until the mid-1990s. This central section still covered
between 81 and 92 % of the UEA CG record for all compounds except
CHF3 (58 %) and HFC-125 (43 %) and provided a suitably long time
series leading up to the stratospheric campaigns (black vertical lines in
Fig. 1) for AoA calculations. We
were left with a time series between 13 and 21 years long (compound
dependent), which compares well to the 10- to 15-year time periods utilised in some previous
studies (Engel et al., 2002, 2006; Stiller et al., 2008). A bootstrap
procedure, outlined below, was used to determine whether polynomial fits were
robust throughout the time period of interest. Two other fit procedures were
compared to the polynomials using Igor Pro software. The cubic spline
interpolation failed to cope with the temporally patchy nature of the UEA CG
time series and the smoothing spline interpolation provided similar results
to the polynomial fits, without the ability to incorporate them into the
bootstrap procedure required for our uncertainty analysis. The mean ages
derived from the fit-interpolated data were also compared to those derived
from the “raw” CG time series, as used in Laube et al. (2013). The
difference between the mean ages derived from these two methods was, for all
compounds except HFC-227ea, a maximum of around 2 months (Supplement
Sect. S2, Table S1), but the uncertainties associated with the fit-derived mean ages was
smaller than those derived from the raw CG dataset (Table S1). As the SIO CG
records had a higher sampling frequency during the period of interest, only
their raw time series – not fitted datasets – were used as inputs into the
AoA routine.
UEA CG time series (6-month time shift), polynomial fits applied to
these time series and associated errors (see inset legend). Details of the
analytical uncertainties in UEA CG time series, application of polynomial fit
and comparison with CARIBIC data are provided in Sect. 2. Vertical black
lines on the x axis show the section that contains the 10-year period leading up to each of the stratospheric
campaigns used during the bootstrap procedure (Sect. 3(a)).
Stratospheric measurements used in this paper were obtained from balloon-
and aircraft-based whole air sampling campaigns that took place between 1999
and 2016 (Table 2). The campaigns covered the polar (B34, K2010 and K2011),
mid-latitude (OB09, SC16) and tropical (B44) stratosphere. For B44, OB09,
K2010, K2011 and SC16 all compounds except CF4 were analysed at UEA on
the same system used to analyse the tropospheric trends with B34
C2F6 samples being analysed on the older version of this
instrument. B34 SF6 data were provided by the Goethe University
Frankfurt. Sample collection and campaign details for OB09, K2010 and K2011
are discussed in Laube et al. (2013) and OB09 and B44 are discussed in
Laube et al. (2010a). The B34 campaign used the same equipment outlined in
B44. For more information on the recent StratoClim campaign (SC16) visit
http://www.stratoclim.org.
Overview of stratospheric campaigns used in this study.
AbbreviationCampaign datesPlatformLocation, altitude*, latitude, longitude,Data availability: where data are available for individual campaigns campaigns, collaborationsthe percent of analytical precision is given. CF4C2F6C3F8CHF3HFC-125HFC-227eaSF6B346 Feb 1999High-altitude balloon-Kiruna, Sweden1.82.1borne whole air samplerUp to 26 km, 62–77∘ N, 1∘ W–29∘ EB4411 Jun 2008Teresina, Brazil2.43.11.5Up to 33.5 km, 5∘ S, 43∘ WLaunched by the French Space Agency,Centre National d'Etudes SpatialesOB0930 Oct 2009M55 Geophysica high-Oberpfaffenhofen, Germany0.80.31.60.54 Nov 2009altitude aircraft10–20 km, 48–54∘ N, 7–12∘ EK2010 UEA20 Jan 2010Kiruna, Sweden0.41.11.40.71.50.7and9–19 km, 62–77∘ N, 1∘ W–29∘ EK2010 SIO2 Feb 2010Part of RECONCILE (von Hobe et al., 2013)0.32.01.4and ESSENCE campaignsK2011 UEA11 Dec 2011 and1.50.61.11.2K2011 SIO16 Dec 20110.42.51.3SC161 Sep 16Kalamata, Greece0.71.30.51.00.80.8and10–21 km, 33–41∘ N, 22–32∘ E6 Sep 2016Part of EU StratoClim project
* Maximum sampling altitude for balloons and cruising altitude range for
aircraft.
A subset of K2010 and K2011 samples were also analysed at SIO using the
Medusa system and calibration scales described above for the AGAGE SIO CG
records. SIO provided data for CF4, C2F6 and SF6. Due to
the low pressure and volumes of these samples, only around 280 mL of sample
were measured, alternated by the same volume of reference gas. The K2010
samples were at a pressure that allowed for analysis via the standard Medusa
method (see references above) using Veriflow clean pressure regulators to
sample 6–12 repeated measurements at roughly constant pressures. Due to the
lower pressure in the K2011 samples these were analysed against an
identically constructed sample flask containing a reference gas at the same
pressure as the starting pressure in each K2011 sample. This allowed for
both the sample and reference gas to be analysed without a regulator and allowed
for concurrent pressure decreases in the sample and calibration flask,
mitigating the possible impact that large differences in pressure between
the ambient and calibration samples may have had on the SIO analysis. Between 3 and 8
repetitions were conducted for the K2011 samples. Analytical precisions for
SIO data are provided in Table 2.
Uncertainties provided for all UEA measurements are a combination of the
analytical precision calculated from repeat analyses of the calibration
standard across each analysis day and the regular (usually daily) paired or
triplicate analysis of individual samples. Samples for which the total
uncertainty was greater than 3 times the standard deviation of the
uncertainties across the entire campaign analysis period were excluded. The
percentages of samples removed across all campaigns were ∼ 4 %
for SF6, CHF3 and HFC-227ea, ∼ 3 % for
HFC-125, 2 % for C3F8 and none for C2F6. Datasets
provided by other institutions (University of Frankfurt B34 SF6 and SIO
K2010 and K2011 data) were smaller and could therefore not be quality
controlled in this manner; all data provided to us were included in further
analyses.
A sample of stratospheric air represents a mixture of air masses with
different transport histories and thus different ages. This distribution of
transport times is the “age spectrum”, a probability density function for
which the first moment, or mean, is the mean age for that parcel and the
second moment, or variance, is the width of the age spectrum (Hall and Plumb,
1994). Mean ages were calculated using the method described in Engel et
al. (2002) based on the method provided for inert tracers by Hall and Plumb (1994).
This method has been further discussed and modified in various
publications, including Engel et al. (2006, 2009), Bönisch et
al. (2009) and Laube et al. (2013). Where we use or refer to the
methodological tests or variations used in the papers subsequent to Engel et
al. (2002) we will reference these explicitly. To calculate mean age, one
requires a tropospheric trend, stratospheric measurements and an
understanding of the width of the age spectrum. As this study focuses on
assessing potential new age tracers we carefully considered the uncertainties
associated with the mean ages calculated by our AoA routine. This uncertainty
analysis is described in Section 3, where we consider the uncertainties
associated with the main inputs to the AoA routine.
Description of and results from the age tracer uncertainty
assessment
As this study focuses on assessing potential new age tracers we carefully
consider the uncertainties associated with the mean ages calculated by our
AoA routine. Potential sources of uncertainty include
uncertainties in the tropospheric trend,
uncertainties in the stratospheric measurements,
different methods of implementing the tropospheric trend within the AoA
routine, and
different methods for the parameterisation of the width of the age
spectrum.
These four main areas of uncertainty are discussed below. A wider suite of
tests was performed to help us better understand the mean age uncertainties,
many of which have informed our protocol for investigating the main
uncertainty components (a–d) or are referenced in our analysis of these
components in the following text. Section S2 includes a
table (Table S1) which provides an overview of the full suite of uncertainty tests
performed on our dataset.
For each uncertainty analysis a similar procedure was followed. Here the
procedure is outlined using generic terminology, with a specific example for
each step as used in our study.
Task. A component of the mean age calculation was identified and considered as the
base scenario.
Example. We used our Cape Grim raw time series (the grey markers in Fig. 1) as the tropospheric trend input.
Task. The errors associated with this component were
identified.
Example. In our case this means the analytical uncertainty in each of the measurements in the raw time series.
Task. A “min” and a “max” dataset was created using these uncertainties.
Our mean mixing ratio minus the respective analytical uncertainty value provides the “raw_min” dataset.
Example. The addition of the analytical uncertainty provides “raw_max”.
Task. A mean age is calculated for each of our stratospheric air samples using the
base scenario.
Example. Our mean ages were calculated using “raw” as the tropospheric input.
Task. Keeping everything else constant (Table S1) the mean age was calculated again
using the “min” and “max” datasets.
Example. Our mean ages were calculated using “raw_min” and “raw_max” as tropospheric inputs.
Task. The mean ages obtained from “min” and “max” are compared to those from the
base scenario. In our case, the differences between the “min” and “max” cases
are often
plotted as a “residual plot”. The average difference between the “min” and “max”
cases is provided in Table 3 (if one of the key uncertainties) or Sect. S2 (all
tests).
Example. The mean ages derived for each stratospheric measurement using “raw”, “raw_min” and
“raw_max” are compared. The absolute average difference between “raw” and its min–max variants
was 0.5 months for SF6 (case 2 in Table S1).
Uncertainties* associated with calculating the mean age of air
for stratospheric samples.
*These are averages from campaigns B44, OB09, K2010 and K2011
(Table 2). B34 data are not included as the analysis of these samples was performed
on an older instrument (C2F6) or not at UEA (SF6). SC11 data
are not included as a full uncertainty analysis was not performed on SC16
due to the complex air sample source region (Sect. 4).
(a) Uncertainties in the tropospheric measurements
The first class of uncertainties we consider are those associated with the
fit-interpolated tropospheric trend (cases 4 and 5 in Table S1). Here our base
scenario is comprised of mean ages derived from the fit-interpolated tropospheric
trend (hereafter referred to as “fit”) compared to those derived from
“fit_min” and “fit_max”, which we obtained from
a bootstrap procedure (Efron, 1979; Singh and Xie, 2008). No sampling
perfectly represents natural variability and the resampling procedure used
during the bootstrapping is designed to provide an indication of the impact
of this “subsampling effect”. Our bootstrap procedure was performed as
follows.
To enhance our representation of atmospheric variability, we first took our
CG time series (Table 1) and converted it to a 3n dataset comprised of [original_data] +
[original_data_minus_analytical_uncertainty] +
[original_data_plus_analytical_uncertainty]. However, we only resampled a
dataset of the original size.
We used the bootstrap macro for Microsoft Excel provided by Barreto and
Howland (2006) to resample (with replacement) our CG dataset. A polynomial
fit was applied to each resample.
After 1000 iterations, the standard deviation of the fit parameters was
calculated.
The standard deviation from the bootstrapping procedure was used to create
“fit_min” and “fit_max” datasets which could
be used as tropospheric inputs to the AoA routine.
The ±1 standard deviation uncertainties from this procedure are
plotted as dark blue lines in Fig. 1. The uncertainties associated with the
fits are small and show that the polynomials are robust throughout the
section of the trend used as an input into the AoA routine. The mean ages
resulting from “fit_min” and “fit_max” were
compared to the original mean age values to give an uncertainty estimate for
the tropospheric trend components of the AoA routine (Table 3). Average
uncertainties were around 1–3 months. There are some higher values for
C3F8 and HFC-227ea due to the poorer data coverage in the late
2000s causing the fit to be slightly less robust. This highlights the
importance of ongoing, reliable and regular tropospheric time series
measurements for potential new age tracers. These uncertainties will be
combined into an overall uncertainty for each species later in the
paper.
(b) Uncertainties in the stratospheric measurements
As with the tropospheric trends, “stratmin” and “stratmax” datasets based on
our measurements ± the analytical uncertainties were used as inputs
into the AoA routine and the outputs compared to mean ages derived from the
original stratospheric mixing ratios (cases 8 and 9 in Table S1). Results from
this comparison are shown as a residual plot in Fig. 2, in which the residuals
are the differences between the mean age calculated using our original
stratospheric mixing ratios and those from “stratmin” and “stratmax”. The
impact of the stratospheric measurement uncertainty is larger than for the
tropospheric inputs: roughly double for CF4, C2F6, CHF3,
HFC-227ea and SF6 and similar for C3F8 and HFC-125, but
generally averaged around half a year or less for all compounds (Table 3).
Differences between different compounds can be attributed to a combination
of their growth rates and their stratospheric measurement precision (Table 2).
The ratio of the stratospheric measurement precision to the growth rate
impacts our mean age resolution: uncertainties derived from our
stratospheric measurement precision will be greater if the growth rate is
smaller. The growth rate of C2F6 was slowing (Fig. 1) in the
period leading up to our 2009–2011 campaigns and this contributes to the
larger uncertainties associated with C2F6 compared to other
compounds, despite similar analytical precisions (Table 2). For
C2F6 and SF6 there are both UEA and SIO values (Fig. 2; cases
35 and 36 in Table S1). The mean ages derived from stratospheric samples analysed
by SIO are independent of the UEA measurements, having been calculated using
AGAGE-based tropospheric trends and uncertainties. There are some higher SIO
C2F6 residual values linked to the higher analytical uncertainty
for the SIO measurements (Table 2). This increased uncertainty is not
unexpected: C2F6 is the least abundant of the three gases measured
by SIO for this study and their analytical system is designed for air
samples an order of magnitude, 2 L versus 280 mL, larger than what is
available from stratospheric samples. SF6 measured at both UEA and SIO
showed similar stratospheric uncertainties. Independent verification adds
significant weight to the suitability of these new compounds for use as age
tracers. The larger impact of uncertainties in stratospheric data compared
to the tropospheric trend (Table 3) highlights the importance of precise
measurements of these compounds if they are to be suitable age tracers.
These stratospheric uncertainties are combined with uncertainties from Sect. 3(a) to create an overall uncertainty later in the paper.
Residual plots showing the uncertainties associated with varying
the stratospheric measurement inputs for the AoA routine. The x axis shows the
difference between the mean ages calculated using a minimum and maximum
stratospheric mixing ratio compared to using the mean mixing ratio normally
used, the mean age of which is on the y axis (Sect. 3(b); cases 8 and 9 in Table S1).
The marker shape denotes which institution performed the analysis and the marker
colour the stratospheric campaign; see inset legend. The vertical axis labels
for each row are in the left panel.
(c) Comparing different methods for implementing the tropospheric time
series component of the mean age calculation
One limitation of the AoA routine used in this study is that only a quadratic
function can be used when fitting the tropospheric time series for the AoA
calculation. A recent improvement is to calculate AoA by using a numerical method
that uses the convolution of the age spectra approximated by an inverse
Gaussian distribution with the tropospheric time series (Ray et al., 2017),
which overcomes the limitations of a quadratic fit to approximate such
trends. We implemented this numerical convolution method in our AoA routine
so that we could compare mean ages derived from our data using both the
original quadratic and the numerical convolution algorithms (case 18 in Table S1).
The resulting “residual plot” can be seen in Sect. S3 and the average uncertainties in Table 3. We found that outside of very young
(< 1 year) mean ages the difference between these two methods was
1 month or less. The weaker performance near the tropopause is a known problem
of the convolution method for younger mean ages, which require the
convolution over a short time period, potentially leading to mean age biases
due to observed short-term variability and/or data sparsity. As the quadratic
method performed better across the whole range of mean ages in our study, we
use that method to derive mean ages and uncertainties discussed in all
subsequent sections of the paper.
(d) Uncertainty in parameterisation of width of age spectrum
As described in Engel et al. (2002), stratospheric mixing ratios cannot
simply be calculated by propagating the tropospheric trend into the
stratosphere: due to non-linearities in the tropospheric trends for our
compounds of interest, the width of the age spectrum impacts the propagation
of these trends. As the width of the age spectrum cannot be measured
directly, we assume a constant value of 0.7 as the parameterisation of the
ratio widthagespectrum2meanage (from Hall and Plumb,
1994, as used in Engel et al., 2002 and Laube et al., 2013). Previous studies
have investigated the effect of varying this parameterisation. Engel et
al. (2002) investigated the impact of using values of 0, 0.7 and 1.25 and
found differences of less than half a year for CO2 and SF6 mean
ages. They also reported that the best agreement between these two age
tracers was reached when using 0.7. Laube et al. (2010b) also tested the
impact of this value on calculated fractional release factors (FRFs; see
Sect. 5), comparing values of 0.5, 0.7 and 1.25, and found that this factor had a
small impact on the FRF for a range of long-lived halocarbons. As this study
introduces new potential age tracers, investigating the impact of this
parameterisation is pertinent. Values of 0.5 and 1 were compared to the
commonly used value of 0.7 (residual plot in Sect. S3). The results are shown in
Table 3: one can see that the impact is small (< 1 month on average)
compared to the impact of (a) and (b) and is similar for all compounds.
Combination of errors and analysis of new age tracers
The two key uncertainties from Sect. 3, namely those associated with the
tropospheric trend and stratospheric measurements (columns a and b in Table 3),
were combined and used as the error bars in Fig. 3, which shows a
vertical profile of the mean ages derived from all six of our tracers. We
use CFC-11 instead of height or potential temperature as a vertical
coordinate because it has a well-quantified vertical distribution (Hoffmann
et al., 2014) influenced by the same localised transport and mixing
processes as our observed age tracers. Tropospheric CFC-11 mixing ratios
have slowly declined in the period covered by the stratospheric campaigns
(1999–2011) at a rate of between 0.5 and 1 % per year (based on our CG trend).
A linear fit of the data throughout this period was relatively robust:
∼ 3 % standard deviation between fits calculated over eight
different time windows and R2 values of > 0.99 for all eight
fits. Based on this we corrected the CFC-11 mixing ratios for the
stratospheric campaigns relative to the earliest (B34 in 1999) campaign.
This is a simplification, as the propagation of tropospheric mixing ratios
into the stratosphere is influenced by the width of the age spectrum (see Sect. 2).
As the CFC-11 mixing ratios are not used in further calculations (purely
as a visual indicator of altitude) and the trend during the time period
covered is linear and small, we felt it a suitable approximation for our
needs.
Vertical profiles of mean ages derived from all compounds used in
this study. Panels (b) and (c) show the same data as in (a) but split into
polar (b) and mid-latitude and tropical (c) flights only (see Table 2 for
campaign details). Colours represent different age tracers (see inset
legend) and remain the same across all panels.
“Best-estimate” mean ages (a combined mean age based on CF4,
C2F6, C3F8, CHF3 and HFC-125) plotted against
SF6 mean age. Error bars are based on stratospheric uncertainties from
Table 3 column b. All fits are bivariate linear fits with uncertainties
shown by shaded areas (see inset legend). SF6 vs. CO2 line from
Andrews et al. (2001) included for comparison.
As mentioned before, a suitable age tracer must have a well-quantified,
monotonically changing tropospheric trend, precise stratospheric measurements
and be relatively inert in the stratosphere. The suitability of our new age
tracers to meet the first two requirements is shown by the error bars in Fig. 3
and the final column in Table 3. The uncertainties of the new age tracers
were compared to those associated with SF6 and were found to be similar
for C3F8 and HFC-227ea, smaller for HFC-125 and larger but within
a similar magnitude range for CF4, C2F6 and CHF3. In this
respect, these new age tracers are as suitable as the commonly used tracer
SF6. As for the final point, that the compounds are inert in the
stratosphere (suggested by their lifetimes; see Table 1), this is also
supported by Fig. 3 in which we can compare the mean ages derived from the new
tracers to those derived from SF6. It is interesting that SF6
(current lifetime estimate 3200 years) lies to the right of the plot, the
trend line in Fig. 3a overlapping with HFC-227ea (stratospheric lifetime
estimated at 673 years). This high bias in SF6-derived mean ages
supports the recently revised SF6 lifetime estimate of 850 (580–1400)
years (Ray et al., 2017). The other compounds tend to give younger mean ages
consistent with longer stratospheric lifetimes. In particular, HFC-125 shows
evidence of having a stratospheric lifetime well in excess of 351 years (see
Sect. 1). Loss of SF6 may be understandable in the polar regions during
winter due to the mesospheric sink and the downward transport of SF6-depleted mesospheric air within the polar vortex, but when we split our
results into polar (Fig. 3b) and mid-latitude and tropical (Fig. 3c) flights
one can see that the SF6 fit still mimics that of HFC-227ea, suggesting
that there is evidence even in this region that SF6-derived mean ages may
be more consistent with the shorter-lived HFC-227ea. This raises the question
as to whether the sink of SF6 is indeed exclusively located in the
mesosphere, although admittedly our non-polar dataset is limited and we
cannot rule out mixing of polar vortex air (or vortex remnants) being
observed in mid-latitudes outside of the winter polar vortex (Strunk et
al., 2000).
Table 4 shows the degree of agreement within stratospheric measurement
uncertainties (column b in Table 3) of the mean ages derived from each of
the age tracers. There is strong agreement between all the new age tracers:
CF4, C2F6, C3F8, CHF3 and HFC-125. Mean ages
derived from these compounds, except for CHF3, do not agree well with
the mean ages derived from SF6 and HFC-227ea. With the lifetime of
CHF3 in the middle of our range of tracer lifetimes (Table 1) we would
expect CHF3-derived mean ages to agree with both shorter- and
longer-lived compounds. There is good agreement between HFC-227ea and
SF6. Table 4 also shows the degree of agreement when the data are split
into polar and mid-latitude and tropical datasets. There are fewer data for
the latter group for which we have co-measurements of two or more age tracers.
However, there is still good evidence that the agreement between SF6
and HFC-227ea is stronger than for SF6 and the new age tracers.
Percentage of samples for which the mean
age derived from two tracers agreed within the uncertainties*.
We combined the results from the new age tracers (CF4, C2F6,
C3F8, CHF3 and HFC-125) to derive a new “best estimate” of the
mean age of air and plotted this against the SF6 mean age in Fig. 4. As
we may expect different results in the tropics, the input region to the
stratosphere, we have removed our four tropical measurements from our dataset
and this slightly reduced dataset is listed as “all (no tropical)” hereafter.
A bivariate linear regression is included for the whole (no tropical)
dataset. Bivariate regression fits using only polar, mid-latitudinal or
tropical data (also in Fig. 4) do not result in significantly different
slopes (although the tropical fit exhibits large uncertainties as it is based
on four points only). Both Figs. 3 and 4 show that the agreement between
SF6 and the other tracers weakens for older mean ages. This is similar
to the relationship between mean ages derived from CO2 and SF6,
which has been shown to be “excellent” for mean ages up to 3 years by
Andrews et al. (2001) and to agree within errors (within a < 0.6-year
difference) with Engel et al. (2002). Interestingly, although we
do not have CO2 data for our campaigns, the slope in Fig. 4 is remarkably
similar to the ∼ 0.8 : 1 slope derived by Andrews et
al. (2001), who compared mean ages of air derived via SF6 and CO2.
Within our “all (no tropical)” dataset, our best-estimate mean age agreed
within uncertainties with the SF6-derived mean age 63 % of the time for
mean ages < 4 years, 42 % of the time within the Engel et
al. (2002) window of 2–5 years and only 16 % of the time above 5 years. Our
results suggest that care should be taken when using SF6 as an age
tracer for older (high-altitude) air where its loss processes (Sect. 1) may
bias derived mean ages. The smaller sample size with mean ages less than 3 years
(n= 33 compared to n= 112 over 3 years) makes it difficult to
conclude if this bias exists in samples with SF6-derived young mean
ages. However, Fig. 4 shows that when the fit is applied only to samples with
SF6 mean ages < 3 years, it is for the most part similar
(within uncertainties) to that derived from the complete dataset.
Figure 4 also includes SC16 data from recently analysed mid-latitude data from two
aircraft flights in the Mediterranean region (Table 2). Stratospheric
uncertainties (as outlined in Sect. 3(b)) were calculated for SC16 samples in
the same manner as for other compounds. As our existing selection of
high-altitude campaigns only included two mid-latitude and one tropical
flight (the latter comprised of only four data points) we thought it
important to include these data. However, the SC16 samples are not discussed
in the error analysis above for two reasons. Firstly, the target of this
campaign was to sample polluted air from the Asian monsoon outflow. The
impact of pollution can be seen in the high levels of several gases,
including SF6, near the tropopause (all but three samples were
collected at potential temperatures > 380 K). Secondly, the
estimation of mean ages near the tropopause is limited by the availability
of our CG-based tropospheric trend, which currently ends in February 2017.
As that trend needs to be shifted by 6 months to account for
interhemispheric transport (see Sect. 2) it only just extends to the time of
these flights, increasing the uncertainties associated with the polynomial
fits (Sect. 2). As high levels of SF6 or other age tracers biases the
derived mean ages toward younger values, the more uncertain mean ages
(< 0.5 years) were removed for Fig. 4 and further analysis. Despite
these differences, the slope of SF6-based vs. best-estimate-based mean
ages for SC16 is similar to that of the other campaigns.
Implications for policy-relevant parameters
Younger mean ages do have implications for three important policy-relevant
parameters that are used to quantify the impact of halocarbons on
stratospheric ozone:
stratospheric lifetimes of ODSs;
FRFs (the fraction of a halocarbon that has been converted into its reactive
(ozone-depleting) form in the stratosphere; compounds with larger FRFs
result in greater ozone depletion); and
ODPs (a measure of the impact of individual halocarbons to deplete ozone
relative to CFC-11).
In Laube et al. (2013) these three parameters were calculated using
SF6-based mean ages. Here we revisit the Laube et al. results
and
calculate updated FRFs, lifetimes and ODPs using our new best-estimate
mean age derived from our five new age tracers for the following 10 ODSs:
CFC-11, CFC-113, CFC-12, HCFC-141b, HCFC-142b, HCFC-22, Halon-1301,
Halon-1211, carbon tetrachloride (CCl4) and methyl chloroform
(CH3CCl3). CFC, halon and HCFC formulas are given in Table 5. We
also compare these results to the WMO (2014) recommendations.
Updated stratospheric lifetimes based on “best-estimate” mean ages
derived in this study compared to existing literature values.
* All lifetimes calculated using CFC-11 lifetimes of 60 years, with
CFC-11 lifetimes based a on CFC-12 lifetime of 100 (Laube et al., 2013) or 102
(this study) years.
Stratospheric lifetimes derived from new age tracers
The lifetime of the 10 ODSs listed above were calculated in Laube et
al. (2013) using a method dependent on the slope of the correlation between
CFC-11 mixing ratios and mean ages at the tropopause. When using the new
best-estimate mean age this slope changes from -20.6 ± 4.6
to -28.6 ± 4.3 ppt yr-1. The updated
stratospheric lifetimes calculated from our new slope are shown in Table 5
alongside the old values and recommendations from WMO (2014). In
WMO (2014) the stratospheric lifetimes are taken from model mean values (with
the exception of CCl4, for which they used tracer and model mean data) from
SPARC (2013). As our lifetime calculation only produces lifetimes relative to
that of CFC-11, changes are generally small. The exceptions are the three
main hydrochlorofluorocarbons (HCFCs), for which the lifetime has decreased
significantly compared to Laube et al. (2013), and CH3CCl3 for
which it has increased. Both changes bring our estimations closer to those of
WMO (2014). This is linked to the relatively large changes (increases for
HCFCs and a decrease for CH3CCl3) in the tropospheric abundances of
these gases in recent years.
Fractional release factors derived from new age tracers
Two updates to the calculations of FRFs reported in Laube et al. (2013) were
made, and the resulting FRFs can be seen in Table 6 alongside the original
Laube et al. results and WMO (2014) values based on observation-based FRFs
from Newman et al. (2007). The first change was to use our new best-estimate mean age in the FRF calculation. The second change was to use the
new methodology outlined in Ostermöller et al. (2017). Plumb et al. (1999) presented a new formula to calculate FRFs that
considers the dependency of the age spectrum on the stratospheric lifetime
and tropospheric trend of the ODS in question. We applied this correction
using the exact parameterisation suggested by Plumb et al. (1999). We note
that some of the lifetimes used by Plumb et al. are somewhat different to
ours, but tests on the influence of lifetime on FRFs derived from this
parameterisation showed that the impact was limited to ±0.03, which is
well within our FRF uncertainties (Table 6). Changes from the initial mean
age correction are significant and would result in increased FRFs throughout.
However, these two corrections can have contrary effects for species with
strongly increasing (e.g. HCFC-22; Fig. 5b) or decreasing (CH3CCl3;
Fig. 5c) tropospheric abundances. For HCFC-22 the two corrections work in the
same direction, resulting in substantially higher FRFs at a given mean age.
For CH3CCl3 the opposite is true and we see very little change.
Updated mid-latitude FRFs based on our “best-estimate” mean ages
(taken at 3 years) derived in this study compared to existing literature
values.
Changes in FRFs resulting from our new “best-estimate” mean age of
air and the improved FRF calculation method from Ostermöller
et al. (2017) for OB09, K2010 and K2011 compared to previously published K2010
and K2011 data (Laube et al., 2013) and FRF mean age correlations from
Newman et al. (2006). Shown for three compound case studies; see details in
the
main text of the paper.
Updated ODPs based on “best-estimate” mean ages (taken at 3 years)
derived in this study compared to existing literature values. Numbers in brackets are min–max* values.
This study ODP% differenceWMOLaube et al.Compoundrelative to WMO(2014)(2013)CFC-111, by definition–11CFC-1130.68 (0.61–0.76)-200.810.63 (0.57–0.69)CFC-120.70 (0.62–0.79)-150.730.67 (0.59–0.75)HCFC-141b0.083 (0.069–0.10)-180.1020.063 (0.051–0.076)HCFC-142b0.037 (0.031–0.043)-340.0570.019 (0.015–0.025)HCFC-220.028 (0.022–0.035)-170.0340.019 (0.015–0.025)Halon-130119.0 (17.0–22.0)-2515.2018.7 (17.0–20.3)Halon-12115.51 (4.89–6.24)-206.905.8 (5.2–6.5)CCl40.92 (0.80–1.05)280.720.82 (0.77–0.87)CH3CCl30.13 (0.11–0.14)-110.140.14 (0.13–0.16)
* Min and max values derived from min and max lifetimes and FRF
values from Tables 5 and 6. Based on a CFC-11 lifetime of 60 years.
Ozone depletion potentials derived from new age tracers
ODPs were calculated relative to CFC-11 using the method in Laube et
al. (2013) but with updated tropospheric lifetimes from WMO (2014), the
latter mainly affecting compounds with significant removal in the
troposphere. As ODPs were calculated relative to CFC-11 (FRF changes shown in
Fig. 5a), changes to ODPs are only significant for the three
hydrofluorocarbons (HCFCs), which have strong positive trends and thus the
largest changes to their FRFs. Our full set of updates can be seen in Table 7.
The new HCFC ODP values are now closer to the recommended values in WMO (2014),
and we see agreement between HCFC-141b and HCFC-22 within our
uncertainties. Nevertheless, for all other ODSs except CH3CCl3, we
still find ODPs significantly different to the ones used in WMO (2014). This
is even the case when we increase our CFC-11 lifetime to 60.2 years, the
equivalent of assuming a CFC-12 lifetime of 102 years as recommended in WMO (2014).
However, WMO (2014) values are based on Newman et al. (2007) and do
not include the recent correction by Ostermöller et al. (2017). What is
also noteworthy from Fig. 5 is that the discrepancy between the FRF mean
age correlations reported in WMO (2014) and Laube et al. (2013)
largely disappears with our updates. This confirms the suspicion mentioned in
Laube et al. (2013) that this discrepancy might predominantly arise from the
use of different age tracers (WMO, 2014 used CO2-derived mean ages).
Conclusions
We have presented tropospheric trends and stratospheric measurements of seven
trace gases and evaluated their capability to estimate stratospheric mean
ages, which are useful proxies for stratospheric transit times. We find that
these gases have suitable tropospheric growth rates and measurement
precisions (< 2 % for all compounds across all stratospheric
campaigns) for this purpose. A comprehensive uncertainty analysis was
performed on several factors contributing to the uncertainties in
tracer-derived mean ages. Uncertainties in AoA estimates based on our new
tracers were approximately equal to or less than 6 months for all compounds,
which are similar to those for the existing tracer SF6. In addition, independent
analysis of three gases (CF4, C2F6 and SF6) at SIO using
different calibration scales and independent tropospheric trends resulted in
very similar mean ages. Importantly, five of these gases, CF4,
C2F6, C3F8, CHF3 and HFC-125, produce very similar
mean ages of air, allowing us to produce a new best-estimate mean age which
we compared to SF6-derived mean ages. Whilst our non-polar dataset is
limited, we provide some qualitative evidence to suggest potential SF6
loss outside of the polar vortex and support recent work which suggests a
reduction in the SF6 stratospheric lifetime from 3200 to 850 years (Ray
et al., 2017). The discrepancy between SF6- and best-estimate-derived
mean ages is greater for older air, as seen for the CO2–SF6
relationship in Andrews et al. (2001), Engel et al. (2002, 2006) and Ray et
al. (2017), although somewhat in disagreement with Strunk et al. (2000),
who found that SF6 and CO2 mean ages were consistent up to mean
ages of around 7–8 years. Further data from stratospheric balloon and
aircraft flights are needed to answer this question in the future.
The new tracers identified here are not meant to replace SF6 and
CO2, which are established age tracers with well-defined tropospheric
trends and a wealth of stratospheric measurements, particularly as they are
measurable by satellite (Stiller et al., 2008). CO2, in
particular, also has an extremely long stratospheric lifetime. However, the
fact that multiple tracers suggest that SF6 mean ages have a high bias
suggests a need for caution when using SF6 to derive mean ages,
especially above the lowermost stratosphere. We also note that, unlike
CO2, our new age tracers do not have large seasonal cycles or
stratospheric sources and are therefore better suited as tracers of transport
times in the lower stratosphere. As future changes to the BDC are likely to
be complex, a suite of tracers may be better suited than SF6 or CO2
alone in diagnosing long-term changes.
Finally, we use a new tracer-derived best-estimate mean age and
investigate the knock-on effects on policy-relevant parameters such as
stratospheric lifetimes, FRFs and ODPs of 10 important ODSs. A substantial
decrease in the lifetime estimates for HCFC-22, HCFC-141b and HCFC-142b and an
increase in that of CH3CCl3 are observed when compared to the
previous SF6-age-based estimate of Laube et al. (2013). These changes
do not cause large changes to the total atmospheric lifetimes of these
gases; however, their main sink is the reaction with the OH radical in
the troposphere. Our FRF and ODP calculations were further improved by the
addition of a recent correction presented in Ostermöller et al. (2017).
The interaction between these corrections is complex, but again it only
results in substantial (but within ODP calculation uncertainties) changes
for the three HCFCs (larger ODPs) and CH3CCl3 (smaller ODP)
compared to Laube et al. (2013). Changes for all four compounds place our
ODP estimates closer to the recommended ODPs in WMO (2014) than the values
published in Laube et al. (2013).
Raw data used in this paper are comprised of the following:
(1) UEA Cape Grim time series for C2F6, C3F8, CHF3,
HFC-125, HFC-227ea and SF6 – these data are included in the
supplementary material attached to this paper; (2) SIO Cape Grim time series
for CF4, C2F6 and SF6 – these data have been published in the supplementary
material for Trudinger et al. (2016); (3) the UEA and Goethe University Frankfurt stratospheric
measurements of C2F6, C3F8, CHF3, HFC-125, HFC-227ea
and SF6 taken during the high-altitude balloon and aircraft campaigns (see
Table 2) – these data are included in the Supplement; and (4) the SIO CF4,
C2F6 and SF6 stratospheric measurements from the Kiruna 2010
and 2011 Geophysica aircraft campaigns – these data are included in the
Supplement.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-3369-2018-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
Johannes Laube received funding from the UK Natural Environment Research
Council (Research Fellowship NE/I021918/1) and David E. Oram from the
National Centre for Atmospheric Science. Part of this work was funded by the
ERC project EXC3ITE (EXC3ITE-678904-ERC-2015-STG). We acknowledge the
Cape Grim staff over many years for the collection of the Cape Grim air
archive and for collecting air samples for UEA. Funding for the Cape Grim air
archive is from CSIRO, the Bureau of Meteorology and Refrigerant Reclaim
Australia. We thank Michel Bolder for collecting the Geophysica air samples
and acknowledge the work of the Geophysica aircraft and CNES balloon teams as
well as related funding from ESA (PremierEx project), the Forschungszentrum
Jülich, the European Commission (FP7 projects
RECONCILE-226365-FP7-ENV-2008-1 and
StratoClim-603557-FP7-ENV-2013-two-stage) and the Dutch Science Foundation
(NWO; grant number 865.07.001). The operation of the AGAGE instruments at SIO
is supported by the National Aeronautics and Space Administration (NASA; grants NAG5-12669, NNX07AE89G and NNX11AF17G to MIT and grants NNX07AE87G,
NNX07AF09G, NNX11AF15G and NNX11AF16G to SIO). Edited by: Gabriele Stiller Reviewed by:
three anonymous referees
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