ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-10501-2016Satellite observations of stratospheric hydrogen fluoride and
comparisons with SLIMCAT calculationsHarrisonJeremy J.jh592@leicester.ac.ukChipperfieldMartyn P.https://orcid.org/0000-0002-6803-4149BooneChristopher D.DhomseSandip S.https://orcid.org/0000-0003-3854-5383BernathPeter F.FroidevauxLucienAndersonJohnRussell IIIJameshttps://orcid.org/0000-0002-4835-7696Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UKNational Centre for Earth Observation, University of Leicester, Leicester LE1 7RH, UKInstitute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UKNational Centre for Earth Observation, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UKDepartment of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, CanadaDepartment of Chemistry and Biochemistry, Old Dominion University, Norfolk, Virginia 23529, USAJet Propulsion Laboratory, Pasadena, California 91109, USADepartment of Atmospheric and Planetary Sciences, Hampton University, Hampton, Virginia 23668, USAJeremy J. Harrison (jh592@leicester.ac.uk)22August20161616105011051922October20158December201527June201618July2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/10501/2016/acp-16-10501-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/10501/2016/acp-16-10501-2016.pdf
The vast majority of emissions of fluorine-containing molecules are
anthropogenic in nature, e.g. chlorofluorocarbons (CFCs),
hydrochlorofluorocarbons (HCFCs), and hydrofluorocarbons (HFCs). Many of
these fluorine-containing species deplete stratospheric ozone and are
regulated by the Montreal Protocol. Once in the atmosphere they slowly
degrade, ultimately leading to the formation of hydrogen fluoride (HF), the dominant reservoir of
stratospheric fluorine due to its extreme stability. Monitoring the growth of
stratospheric HF is therefore an important marker for the success of the
Montreal Protocol.
We report the comparison of global distributions and trends of HF measured in
the Earth's atmosphere by the satellite remote-sensing instruments ACE-FTS
(Atmospheric Chemistry Experiment Fourier transform spectrometer), which has
been recording atmospheric spectra since 2004, and HALOE (HALogen Occultation
Experiment), which recorded atmospheric spectra between 1991 and 2005, with
the output of SLIMCAT, a state-of-the-art three-dimensional chemical
transport model. In general the agreement between observation and model is
good, although the ACE-FTS measurements are biased high by ∼ 10 %
relative to HALOE. The observed global HF trends reveal a substantial slowing
down in the rate of increase of HF since the 1990s:
4.97 ± 0.12 % year-1 (1991–1997; HALOE), 1.12 ± 0.08 % year-1
(1998–2005; HALOE), and 0.52 ± 0.03 % year-1 (2004–2012;
ACE-FTS). In comparison, SLIMCAT calculates trends of 4.01, 1.10, and
0.48 % year-1, respectively, for the same periods; the agreement is
very good for all but the earlier of the two HALOE periods. Furthermore, the
observations reveal variations in the HF trends with latitude and altitude;
for example, between 2004 and 2012 HF actually decreased in the Southern
Hemisphere below ∼ 35 km. An additional SLIMCAT simulation with
repeating meteorology for the year 2000 produces much cleaner trends in HF
with minimal variations with latitude and altitude. Therefore, the variations
with latitude and altitude in the observed HF trends are due to variability
in stratospheric dynamics on the timescale of a few years. Overall, the
agreement between observation and model points towards the ongoing success of
the Montreal Protocol and the usefulness of HF as a metric for stratospheric
fluorine.
Introduction
The accumulation of fluorine in the Earth's atmosphere has resulted from
anthropogenic emissions of organic molecules such as chlorofluorocarbons
(CFCs), hydrochlorofluorocarbons (HCFCs), and hydrofluorocarbons (HFCs). The
long atmospheric lifetimes of such molecules allow them to reach the
stratosphere, where they break down and liberate fluorine in various
inorganic forms. The most abundant of the emitted organic source molecules
are CFC-12 (CCl2F2), CFC-11 (CCl3F), CFC-113
(CCl2FCClF2), which are all now banned under the Montreal Protocol
because they deplete stratospheric ozone, and HCFC-22 (CHClF2), the
most abundant HCFC and a transitional substitute under the Protocol.
Although long-lived, these molecules do degrade in the atmosphere at high
altitudes, ultimately to the long-lived stratospheric reservoir molecule
hydrogen fluoride (HF); the chemistry schemes are presented below. Monitoring
HF as part of the atmospheric fluorine family is important in closing the
fluorine budget, particularly as anthropogenic emissions of fluorine
species, many of which are ozone-depleting and all of which are greenhouse
gases, have varied substantially over time. Certainly, monitoring the growth
of stratospheric HF, which has slowed in recent years, is an important
marker for the success of the Montreal Protocol (in addition to monitoring
stratospheric HCl).
For the three most abundant fluorine source gases, CFC-12, CFC-11, and
HCFC-22, atmospheric degradation proceeds with the breaking of a C–Cl
(CFC-12 and CFC-11) or C–H (HCFC-22) bond (Ricaud and Lefevre, 2006):
CCl2F2+hv→CClF2+ClCCl3F+hv→CCl2F+ClCHClF2+OH→CClF2+H2O.
Depending on their structure, the intermediates produced in Reaction (R1) react
further:
CClF2+O2+M→CClF2O2+MCClF2O2+NO→CClF2O+NO2CClF2O+O2→COF2+ClO2,CCl2F+O2+M→CCl2FO2+MCCl2FO2+NO→CCl2FO+NO2CCl2FO+O2→COClF+ClO2.
For CFC-113, and minor sources such as HFCs (e.g. HFC-134a, HFC-152a), the
reaction scheme is similar.
In Reactions (R2) and (R3), carbonyl chloride fluoride (COClF) and carbonyl
fluoride (COF2) are important “inorganic” reservoirs (the common
terminology in atmospheric science differs from that in chemistry) of
fluorine in the stratosphere, with lifetimes of 1.6 (Fu et al., 2009) and
3.8 (Harrison et al., 2014) years respectively; COF2 is more abundant
than COClF. The trends in these inorganic reservoirs over time are directly
related to the trends of the individual source gases. The decrease in the
amounts of atmospheric CFC-11 and CFC-113, the principal sources of carbonyl
chloride fluoride, has led to a decreasing trend in this reservoir (Brown et
al., 2011), whereas carbonyl fluoride is still slowly increasing over time
due to the increase in HCFC-22, which more than compensates for the decrease
in the CFC-12 and CFC-113 source gases (Brown et al., 2011; Harrison et al.,
2014).
COClF and COF2 volume mixing ratios (VMRs) slowly increase with
altitude through the lower stratosphere until they reach their respective
maxima, at ∼ 25–30 km for COClF (Fu et al., 2009) and
∼ 30–40 km for COF2 (Harrison et al., 2014). Above
these altitudes photolysis becomes more efficient, leading to the formation
of fluorine atoms:
COF2+hv→FCO+FCOClF+hv→FCO+ClFCO+O2+M→FC(O)O2+MFC(O)O2+NO→FCO2+NO2FCO2+hv→F+CO2.
The liberated fluorine atoms then react with methane, water, or molecular
hydrogen to form the inorganic product HF. At the top of
the stratosphere most of the fluorine is present as HF (Brown et al., 2014),
the dominant reservoir of stratospheric fluorine due to its extreme
stability. Note that due to this stability, F is not important for catalytic
stratospheric ozone loss. HF is removed from the stratosphere by slow
transport to, and rainout in, the troposphere or by upward transport to the
mesosphere, where it is destroyed by photolysis (Duchatelet et al., 2010).
Overall the amount of HF in the atmosphere is increasing (e.g. Brown et al.,
2014).
The first detection of HF in the Earth's stratosphere, based on solar
spectra recorded from balloon and on the ground at Jungfraujoch, was made by
Zander et al. (1977). Measurements continue to be made at Jungfraujoch using
a ground-based Fourier transform spectrometer (FTS) (e.g. Duchatelet et
al., 2010). There have also been measurements of HF taken, for example, by
the Atmospheric Trace MOlecule Spectrometry Experiment (ATMOS) instrument,
which flew four times on NASA Space Shuttles between 1985 and 1994 (Irion et
al., 2002), and the MkIV interferometer, a balloon-borne solar occultation
FTS (Velazco et al., 2011). Measurements taken by satellite-borne
instruments, however, allow HF to be observed with global coverage and
seasonal and latitudinal variability to be investigated fully. The first
global atmospheric distributions of HF were provided by the HALogen
Occultation Experiment (HALOE) instrument, onboard the Upper Atmosphere
Research Satellite (UARS), which recorded atmospheric spectra between 1991
and 2005. More recently, the Atmospheric Chemistry Experiment FTS (ACE-FTS),
onboard the SCISAT satellite, has been recording atmospheric spectra since
2004, carrying the mantle of HF measurements into the second decade of the
21st century. In fact, the ACE-FTS is the only satellite instrument
currently taking measurements of HF.
This paper follows on from our recent work on the global distributions and
trends of COF2, the most important “temporary” stratospheric fluorine
reservoir that directly leads to the formation of HF. The aim of the present
work is to understand the HF global distribution and trends derived from
satellite observations taken by the HALOE and ACE-FTS instruments. To do
this, we use the SLIMCAT model, a state-of-the-art three-dimensional (3-D)
chemical transport model (CTM), one of the few to include stratospheric
fluorine chemistry. Additionally, we compare tracer–tracer correlations
between some of the major HF “sources” for SLIMCAT and satellite
observations as a further test of the model chemistry.
Hydrogen fluoride datasetsACE-FTS
The ACE-FTS instrument, which covers the spectral region 750 to
4400 cm-1 with a maximum optical path difference (MOPD) of 25 cm and a
resolution of 0.02 cm-1 (using the definition of 0.5/MOPD throughout),
uses the sun as a source of infrared (IR) radiation to record limb transmission
through the Earth's atmosphere during sunrise and sunset (“solar
occultation”). Transmittance spectra are obtained by ratioing against
exo-atmospheric “high sun” spectra measured each orbit. These spectra, with
high signal-to-noise ratios, are recorded through long atmospheric limb paths
(∼ 300 km effective length), thus providing a low detection threshold
for trace species. ACE has an excellent vertical resolution of about 3–4 km
and can measure up to 30 occultations per day, with each occultation sampling
the atmosphere from 150 km down to the cloud tops (or 5 km in the absence
of clouds). The locations of ACE occultations are dictated by the low Earth
circular orbit of the SCISAT satellite and the relative position of the sun.
Over the course of a year, the ACE-FTS records atmospheric spectra over a
large portion of the globe (Bernath et al., 2005), from which it is possible
to extract profiles of many fluorine-containing species, including CCl3F
(CFC-11), CCl2F2 (CFC-12), CHClF2 (HCFC-22),
CCl2FCClF2 (CFC-113), CH3CCl2F (HCFC-141b),
CH3CClF2 (HCFC-142b), CH2FCF3 (HFC-134a), CHF3
(HFC-23), CF4, COF2, COClF, HF, and SF6.
The atmospheric pressure and temperature profiles, the tangent heights of the
measurements, and the HF VMRs were taken from v3.0
(January 2004 until September 2010) and v3.5 (from October 2010) processing
of the ACE-FTS data. Note that the retrieval scheme is identical for both the
v3.0 and v3.5 datasets, the difference being in the meteorological data used
as input for the pressure and temperature retrievals (the lowest ACE-FTS
levels use these data directly). Due to an error in these inputs, v3.0 data
should only be used for measurements taken until the end of September 2010,
while v3.5 is valid for all ACE-FTS measurements. Details of the retrieval
scheme for versions 3.0/3.5 processing have been explained elsewhere (e.g.
Boone et al., 2013; Harrison et al., 2014). Briefly, vertical profiles of
trace gases (along with temperature and pressure) are derived from the
recorded transmittance spectra via an iterative Levenberg–Marquardt nonlinear
least-squares global fit to the selected spectral region(s) for all
measurements within the altitude range of interest. The microwindow set and
associated altitude ranges for the HF retrieval are listed in Table 1. The
VMRs for molecules with absorption features in the microwindow set (see
Table 2) were adjusted simultaneously with the HF amount. All spectroscopic
line parameters were taken from the HITRAN 2004 database (Rothman et al.,
2005), with HF parameters apparently unchanged since HITRAN 1992. The
microwindow set covers eight spectroscopic lines (P1, P2, P3,
P4, R0, R1, R2, R3) from the fundamental (1–0) band
of HF. The HF retrieval extends from a lower altitude of 12 km up to
altitudes corresponding to an atmospheric density of
9.0 × 1015 molecules cm-3, in practice
∼ 50–55 km, thus providing a variation in upper altitude with both
latitude and season (see Table 1). The HF spectral signal in ACE-FTS spectra
recorded above the upper altitude retrieval limit is generally below the
noise level, so it is not possible to retrieve VMRs directly at these
altitudes. Instead, the VMR profile above the highest analysed ACE
measurement is calculated by scaling the “initial” VMR profile, taken from
ATMOS measurements (Irion et al., 2002), over these altitudes; the scaling
factor is determined during the least-squares fitting.
Microwindows for the v3.0/v3.5 ACE-FTS hydrogen fluoride retrieval.
a Included to improve results for interferer HDO.
b Upper altitude given in atmospheric density units, molecules cm-3.
HALOE
Like the ACE-FTS, the HALOE instrument (Russell III et al., 1993) used the
principle of solar occultation to sound the middle atmosphere at sunset and
sunrise (relative to the instrument). HALOE used broadband and gas-filter
radiometry, with channels covering selected portions of the spectrum between
2.45 and 10.04 µm, to determine the mixing ratios of molecules
related to the chemistry of stratospheric ozone and its destruction by CFCs.
In particular, HALOE provided measurements of O3, HCl, HF, CH4,
H2O, NO, NO2, aerosol extinction, and temperature vs. pressure,
over an altitude range of ∼ 15 to 60–130 km depending on channel (HF,
HCl, CH4, and NO were measured using gas filter radiometry). As with the
ACE-FTS, the locations of HALOE occultations and hence the extent of its
global coverage were dictated by its orbit and the relative position of the
sun. HALOE, with an orbit inclination of 57∘ compared with
74∘ for the ACE-FTS, had a more even latitudinal coverage and
provided more data over tropical regions, for example, than the ACE-FTS, which
takes most of its measurements at high latitude.
The atmospheric pressure, temperature, tangent heights, and HF
VMRs were taken from version 19 processing of the HALOE data, which are
available from October 1991 to November 2005. The retrieval scheme
incorporates a simple “onion peeling” approach stabilised at the top and
bottom of the profile with a scalar optimal estimation formulation developed
by Connor and Rodgers (1989). For the HF channel, the spectral bandpass
5 % relative response points are 4025 and 4135 cm-1. The HF
spectral line parameters were taken from the HITRAN 1992 database (Rothman et
al., 1992). The instantaneous vertical field of view in the limb is
∼ 1.6 km. Detailed validation studies for HALOE HF measurements were
published by Russell III et al. (1996). Note that for internal consistency with
previous work on the fluorine budget (Brown et al., 2014) and COF2
(Harrison et al., 2014), the vertical pressure grid has been interpolated
onto the standard 1 km grid used by the ACE-FTS.
GOZCARDS
The ACE-FTS, HALOE, and SLIMCAT model time series are also compared with those
of the GOZCARDS (Global OZone Chemistry And Related Datasets for the
Stratosphere) HF data product in Sect. 5. GOZCARDS provides a global
long-term stratospheric Earth System Data Record (ESDR) for stratospheric
ozone and related chemical species, including HF. The HF data record was not
ready in time to be included in the original dataset provided for public
usage (temperature, O3, H2O, HCl, N2O, and HNO3); we are
presenting the HF data for the first time here. Froidevaux et al. (2015) have
described the GOZCARDS data creation methodology and some stratospheric
characteristics concerning the latter five species. The constituent datasets
are time series of monthly zonal means vs. latitude (in 10∘
latitude bins) taken from existing satellite datasets. In particular, the
GOZCARDS HF data product is derived by merging the v19 HALOE (1991–2005) and
v2.2 ACE-FTS (2004–2010) datasets, with the relative bias between source
datasets removed by averaging them over the overlap period 2004–2005 and
adjusting the series accordingly; note that such a process does not account
for any systematic biases in the original datasets. All GOZCARDS datasets are
provided on a vertical pressure grid. Again, to be consistent with previous
ACE work, this vertical pressure grid has been interpolated onto the standard
1 km grid used by the ACE-FTS. Note that as this GOZCARDS dataset uses v2.2
ACE-FTS data, the time series terminates in September 2010.
Interferers in the v3.0/v3.5 ACE-FTS hydrogen fluoride retrieval.
MoleculeLower altitudeUpper altitudelimit (km)limit (km)H2O1265H18OH1250H17OH1240HDO1225CO21240O31238CH41230OC18O1220N2O1230Retrieval errorsInfrared spectroscopy of hydrogen fluoride
One of the major sources of systematic error for any retrieved atmospheric
profile arises from uncertainties in the laboratory spectroscopic data. A
discussion of spectroscopic errors is therefore appropriate. The ACE-FTS
retrieval makes use of HF line parameters first made available as part of the
HITRAN 1992 database (and remaining unchanged until the HITRAN 2012 release),
with partition data taken from the Total Internal Partition Sums (TIPS)
subroutine included in the HITRAN compilation. HITRAN simply provides error
codes for line parameters in the form of uncertainty ranges but with no
information as to how the parameters are correlated. For the HF line
parameters used in this work, the errors correspond to
0.0001–0.001 cm-1 for the line wavenumber, ν, 2–5 % for the
line intensity, S, and 1–2 % for the air-broadened half-width, γair. Errors are unreported for γself (self-broadened
half-width), nair (temperature-dependent exponent for γair), and δair (air-pressure-induced line shift).
Recently, and after v3.0 processing of the ACE data was complete, HITRAN 2012
became available; it includes a complete re-evaluation of HF spectroscopy.
The associated publication (Rothman et al., 2013) also explains that all the
air-broadening parameters, γair, for the fundamental band of
HF were fitted with the Galatry profile, not the Voigt profile, which is the
lineshape of choice for the HITRAN database. Additionally, the Dicke
narrowing parameters in the original analysis were simply neglected. For the
purposes of this work, we assume a retrieval error of at most
∼ 4 %,
arising from uncertainties in HF line parameters.
ACE-FTS
The ACE v2.2 HF data product, which uses a slightly different microwindow set
from v3.0/v3.5 as well as an earlier version of the PT retrieval, has
previously been validated, for example, against measurements taken by HALOE
and the MkIV interferometer (Mahieu et al., 2008). It was found that ACE-FTS
v2.2 HF measurements are biased high compared to HALOE, with mean differences
around 5–20 % between 15 and 49 km. Comparison of ACE-FTS v2.2 HF with
MkIV data is generally good, with relative differences above 19 km within
±10 %. There have been no detailed comparisons in the literature
between ACE-FTS v2.2 and v3.0 HF datasets, although Duchatelet et al. (2010)
suggest that ACE-FTS v3.0 HF VMRs have decreased by close to 5 % relative
to v2.2. However, a re-evaluation of filtered v2.2 and v3.0/v3.5 HF data
carried out as part of this work indicates good agreement within
±5 %, with no significant overall bias between the two datasets.
The 1σ statistical fitting errors for a single ACE profile are
typically ∼ 5 % over most of the altitude range. These errors are
random in nature and largely determined by the measurement noise of the
ACE-FTS. Averaged profiles tend to be dominated by systematic errors, with
random errors reduced by a factor of 1/√N, where N is the number of
profiles averaged. Spectroscopic sources of systematic error predominantly
arise from the HF HITRAN line list (∼ 4 %; see Sect. 3.1), with
minor contributions from interfering species that absorb in the microwindow
regions; we assume that these contributions are small, at most 1 %, due
to the lack of systematic features in the spectral residuals (Harrison et
al., 2014). Additional systematic errors arise from uncertainties in
temperature, pressure, tangent altitude (i.e. pointing), and instrumental line
shape (ILS); these were estimated by running the ACE-FTS retrieval for a
subset of occultations, with each quantity (bj) perturbed in turn by its
assumed 1σ uncertainty (Δbj). The fractional retrieval
error, μj, is defined as
μj=VMR(bj+Δbj)-VMR(bj)VMR(bj).
Note that pressure, temperature, and tangent height are not strictly
independent quantities for ACE-FTS retrievals; tangent heights are determined
from hydrostatic equilibrium, and so these quantities are strongly
correlated. Therefore, only two of these three quantities are altered:
temperature is adjusted by 2 K and tangent height by 150 m (Harrison et
al., 2014). ILS uncertainty is determined by adjusting the field of view by
5 % (Harrison et al., 2014). A subset of 81 occultations measured between
65 and 70∘ N in July 2010 was selected for this analysis. The
fractional value estimates of the systematic uncertainties, and their
symbols, are given in Table 3. Assuming uncorrelated quantities, the overall
systematic error in the HF retrieval can be calculated as
μsystematic2=μspec2+μint2+μT2+μz2+μILS2.
The total systematic error contribution to the ACE-FTS HF retrieval is
estimated to be ∼ 10 % over the altitude range of the retrieval.
Sources of systematic uncertainty in the ACE-FTS v3.0/v3.5 hydrogen
fluoride retrieval.
As discussed in Sect. 3.1, HF VMRs are not directly retrieved for ACE
measurements taken at tangent heights above the upper altitude limits listed
in Table 1. In the ACE-FTS HF retrieval, the calculated spectrum is generated
from the sum of contributions from the tangent layer up to 150 km. For the
highest analysed measurement, the retrieved VMR in the tangent layer is
generated from the piecewise quadratic interpolation scheme (Boone et al.,
2013), while the VMR in every layer above that is determined from scaling the
“initial” VMR profile, with the scaling factor determined during the
retrieval by forcing the calculated spectrum to match as best as possible the
measured spectrum for the highest analysed measurement. Since the “initial”
profile is fixed to a constant VMR between 50 and 100 km altitude, and since
this portion of the profile is scaled based on the VMR of the highest
analysed ACE measurement, this will likely introduce systematic errors into
the highest altitudes of the retrieved profile. However, since the scaling
factor errors are dominated by the 1σ statistical fitting errors of
the highest analysed ACE measurement, it is anticipated that the systematic
errors at the top of the profiles are reduced when they are averaged to
create zonal means.
HALOE
As discussed in Sect. 3.2, HALOE v19 HF has been validated against ACE-FTS
v2.2, with the ACE measurements biased high by around 5–20 % between 15
and 49 km (Mahieu et al., 2008). Furthermore, HF data from the MkIV
interferometer for three flights (2003–2005) agree well with ACE-FTS, with
relative differences above 19 km within ±10 %, suggesting that there
is a low bias in HALOE. Detailed HALOE HF error estimates and validation
studies have previously been conducted by Russell III et al. (1996). The
estimated errors range from ∼ 27 % at 100 hPa to 15 % at
1 hPa. Actual mean differences between HALOE and balloon data from a series
of nine FTS under-flights, five operating in the far-IR and four MkIV
comparisons in the near-IR, collectively ranged from more than 17 % below
70 hPa, where the mixing ratio is very low to < 7 % above that level,
with no positive or negative bias implied. These HALOE data were produced
using an early algorithm version, but results have proven to be very stable
for later versions.
TOMCAT/SLIMCAT 3-D chemical transport model
SLIMCAT, an offline 3-D CTM, calculates the abundances of a number of
stratospheric trace gases from prescribed source-gas surface boundary
conditions and a detailed treatment of stratospheric chemistry, including
the major species in the Ox, NOy, HOx, Fy, Cly, and
Bry chemical families (e.g. Chipperfield, 1999; Feng et al., 2007). The
model uses winds from meteorological analyses to specify winds and
temperatures. This approach gives a realistic stratospheric circulation
(Chipperfield, 2006; Monge-Sanz et al., 2007). In the version used here the
troposphere is assumed to be well mixed.
For this study SLIMCAT was integrated from 1977 to 2013 at a horizontal
resolution of 5.6∘× 5.6∘ and 32 levels from the
surface to 60 km. The model uses a σ-p vertical coordinate
(Chipperfield, 2006) and was forced by European Centre for Medium-Range
Weather Forecasts (ECMWF) reanalyses (ERA-Interim from 1979 onwards). The
VMRs of source gases at the surface level were specified using datasets
prepared for the WMO/UNEP (2011) ozone assessment. These global mean surface
values define the long-term tropospheric source-gas trends in the model.
Similarly, specification of the surface VMRs of degradation products acts as
a sink for these species. The model initialisation used the estimated
halocarbon loading for 1977, taken from the WMO/UNEP scenarios.
The SLIMCAT run makes use of the same chemistry scheme that was previously
used for our work on COF2 (Harrison et al., 2014); however, in the
present version the photolysis scheme has been updated to use latitudinally
and monthly varying ozone profile shapes in the photolysis look-up table. All
source and degradation products related to fluorine chemistry are listed in
Table 4. COF2 contributions arise from the degradation of CFC-12,
CFC-113, CFC-114, CFC-115, HCFC-22, HCFC-142b, HFC-23, HFC-134a, HFC-152a,
Halon 1211, and Halon 1301, with COClF production arising from the
degradation of CFC-11, CFC-113, and HCFC-141b (CH3CCl2F). Some HF
is assumed to form directly from the source gases (see Table 4), but this
is almost negligible in practical terms (∼ 3 % in 2010, mainly
arising from HFC-134a). The relative amounts of HF formed (in 2010) via COClF
and COF2 are 30 and 67 % respectively.
A comparison between ACE-FTS and SLIMCAT HF zonal means
(September 2009 to August 2010). A full discussion of the seasonal variation
in the HF distribution is provided in the text.
Fluorine source gases in the SLIMCAT chemical scheme and their
atmospheric degradation products.
* These are not source gases, but their degradation products are
included for completion.
The SLIMCAT calculations reveal that at altitudes above the maximum COClF and
COF2 VMRs, there is net loss of these at all latitudes. The primary loss
of COF2 and COClF in the stratosphere occurs via photolysis, with an
additional secondary loss mechanism through reaction with O(1D); SLIMCAT
calculates relative contributions of, respectively, 90 and 10 % for
COF2 (Harrison et al., 2014) and 98 and 2 % for COClF. The SLIMCAT
outputs enable an estimation of the atmospheric lifetimes of COF2 and
COClF by dividing the total modelled atmospheric burden of each species by
the total calculated atmospheric loss rate. The total calculated mean
atmospheric lifetimes are 3.9 years for COF2, revised upwards from the
calculated value of 3.8 years presented by Harrison et al. (2014), and
1.7 years for COClF.
Comparison between ACE-FTS/HALOE/SLIMCAT datasets
The ACE-FTS HF data were binned into five latitude bands by month; VMRs
outside six median absolute deviations (MAD) of the median VMR for each bin
and altitude were removed from the analysis. Once filtered to remove
significant outliers, the data were used to create monthly zonal means at
each altitude within 5∘ latitude bins. In Fig. 1 these have been
plotted next to SLIMCAT zonal means for the months September 2009 to
August 2010, thereby revealing the seasonal variation in the HF distribution
over this period. Note that these dates have been chosen to match those used
in the previous work on carbonyl fluoride (see Fig. 11 of Harrison et al.,
2014). The HF profiles generally show an increase in VMR with altitude, with
the rate of this increase varying with latitude and time of year. Note that
ACE-FTS observations do not cover all latitude bins over a single month (see
Sect. 2.1) and that latitude bins containing fewer occultations are noisier
in appearance. Despite these caveats, Fig. 1 reveals a good agreement between
the ACE-FTS observations and the model, which reproduces very well the
significant seasonal variation. For example, note in particular the agreement
for regions of low HF VMR (< 1000 ppt) at ∼ 30–40 km over the
southern tropics in February 2010 and the northern tropics in August 2010, at
southern middle to high latitudes in December 2009 and March and August 2010,
and at northern middle to high latitudes in February and March 2010.
A comparison between ACE-FTS, HALOE, and SLIMCAT HF zonal
means (September 2004 to August 2005). The ACE-FTS and HALOE time series of
measurements overlap during the period January 2004 to November 2005.
Plots of ACE-FTS and HALOE HF observations side by side with SLIMCAT HF
calculations for September 2004 to August 2005 are shown in Fig. 2. As for
Fig. 1, the agreement between observations and model is generally good and
the significant seasonal variation is well reproduced. Note that, as for the
ACE-FTS, HALOE data do not cover all latitude bins in a given month, although
HALOE does take more measurements at lower latitudes. One noticeable
difference revealed in Fig. 2 is the relative low bias of HALOE measurements
compared with ACE-FTS and SLIMCAT; this is most notable at the top of the
altitude range. Using the period of overlap between ACE and HALOE, we can
estimate that ACE v3.0 is biased high by about 10 % relative to HALOE.
Biases between observations and SLIMCAT will be more fully addressed in
Sect. 6.
The overall atmospheric distribution of HF is determined by a complicated
combination of its production and transport, which depends on the production
and lifetimes of its “sources” COF2 and COClF. Figure 3 shows the
observed and modelled COF2 and COClF zonal means for October 2009 and
February and August 2010. Due to the upwelling of relatively
organic-fluorine-rich air in the tropical regions, the largest VMRs of
COF2 and COClF are found over the tropics (Harrison et al., 2014; Fu et
al., 2009), where solar insolation is highest due to the small solar zenith
angle, at altitudes of ∼ 30–40 and ∼ 25–30 km, respectively. For COF2
the model agrees well with the ACE observations in terms of magnitude and
spatial distribution. For COClF the modelled distribution agrees with ACE but
the peak VMR is overestimated. Analysis of the SLIMCAT simulation shows that
there is net loss of COF2 and COClF at altitudes above those of the
maximum VMRs, at all locations. There is therefore a correlation between the
stratospheric regions of low HF VMR (< 1000 ppt) above ∼ 20 km at
the poles and ∼ 25 km at the Equator and those of peak COF2 and
COClF VMRs.
Figure 1 reveals an asymmetry in the seasonal HF distribution between the two
hemispheres. This is attributable to asymmetries in the distributions of the
“sources” COF2/COClF and their precursors, due to differences in the
meridional Brewer–Dobson circulation, and to the stronger descent of air
associated with the winter polar vortex in the Southern Hemisphere; for
example, compare the enhanced ACE-FTS HF VMRs near the South Pole in
August 2010 with those near the North Pole in February 2010 at
∼ 25 km. An additional source of asymmetry in the COF2
distribution, which directly influences the HF distribution, arises from the
temperature-dependent loss reaction of the COF2-precursor CHClF2
(HCFC-22) with OH, leading to a secondary COF2 maximum at southern
hemispheric high latitudes in the summer mid-stratosphere (∼ 10 K
warmer than the corresponding location in the Northern Hemisphere) (Harrison
et al., 2014); for example, compare the ACE-FTS HF southern hemispheric VMRs
at ∼ 30–35 km in January 2010 with those in the Northern Hemisphere
in July 2010.
A comparison of COF2 and COClF zonal means from
ACE-FTS and SLIMCAT for October 2009 and February and August 2010.
Correlation plots between coincident CFC-12 and COF2
ACE-FTS observations and SLIMCAT calculations for November 2009/July 2010
65–70∘ S and January/May 2010 65–70∘ N.
The error bars represent the standard deviations in the ACE-FTS VMRs.
In addition to side-by-side comparisons of model and observation, the
chemistry scheme in SLIMCAT can be tested by comparing (chemically related)
tracer–tracer correlations for model and observation; only ACE measurements
of fluorine-containing HF “precursors” are available for this purpose. It
is widely known that all long-lived species in the stratosphere have compact
correlations, even when there is no chemical link between them. As explained by
Plumb and Ko (1992), two tracers with lifetimes longer than quasi-horizontal
mixing timescales should be in “slope equilibrium” and produce a compact
correlation. Species with lifetimes longer than vertical transport timescales will also be in “gradient equilibrium” and the compact correlation
will be linear. Furthermore, relative lower-stratospheric lifetimes of
long-lived species (with stratospheric sinks via photolysis or reaction with
O(1D)) under gradient equilibrium can be derived from the linear slope
of the tracer–tracer correlation (Chipperfield et al., 2014).
Correlation plots between coincident CFC-11 and COClF
ACE-FTS observations and SLIMCAT calculations for November 2009/July 2010
65–70∘ S and January/May 2010 65–70∘ N.
The error bars represent the standard deviations in the ACE-FTS VMRs.
In the lower stratosphere COF2 and COClF can be regarded as long-lived
tracers (local lifetimes of many years). Therefore, their tracer isopleths
should follow the typical tropopause-following contours of any long-lived
tracer. In this sense, COF2 and COClF are analogous to NOy, which is
produced from N2O. Figure 4 contains correlation plots between COF2
and its major source, CFC-12, over the two latitude bands 65–70∘ S
and 65–70∘ N for 2 months each over the period
September 2009–August 2010. Comparisons are made at high latitudes, where
ACE-FTS observations are more plentiful, and for individual months to ensure
that time trends in the source-gas VMRs are minimised. The figure reveals
that COF2 is indeed long-lived enough to show a good anti-correlation
with CFC-12 in the lower stratosphere. Furthermore, agreement between the
model and observations is good although there are a few discrepancies around
the region of maximum COF2 VMR; these are due to issues surrounding the
scaled a priori used in the retrieval for this altitude region of the profile
where the spectral signal has dropped to within the noise level (refer to
Harrison et al., 2014, for more details).
Correlation plots between coincident ACE-FTS observations
and SLIMCAT calculations of total “major” organic fluorine, based on CFC-11,
CFC-12, and HCFC-22, and total inorganic fluorine, Fy, for November 2009/July 2010 65–70∘ S and January/May 2010
65–70∘ N. The error bars represent the standard deviations
in the ACE-FTS VMRs.
The HALOE, ACE-FTS, and SLIMCAT HF time series for
selected altitude–latitude bin combinations. Observations are plotted
between October 1991 and December 2012. Overlaid are the time series from a
SLIMCAT run with dynamics arbitrarily annually repeating those for the year
2000; this provides a clearer signal of the long-term chemical changes
without the complication of variations in stratospheric dynamics.
Figure 5 contains correlation plots between COClF and its major source,
CFC-11, for the same conditions as in Fig. 4. Unlike for COF2/CFC-12,
the agreement between model and ACE-FTS is particularly poor and the model
overestimates the peak observed values of COClF; this can also be observed in
Fig. 3. There are several possible reasons for this. Firstly, as the modelled
VMRs are ∼ 50 % higher than the ACE-FTS VMRs, the modelled COClF
lifetime might be too long, i.e. the model underestimates the COClF loss
processes. This would result in the calculated HF VMRs being slightly lower
than they should be, probably by less than a percent, but certainly by less
than the uncertainty of the ACE-FTS measurements. An additional SLIMCAT
calculation with the COClF lifetime lowered by a third does improve the
agreement with observations. Secondly, the COClF sources might be
overestimated, but SLIMCAT calculations for CFC-11 reveal good agreement
with ACE-FTS observations, generally within 10 % (Brown et al., 2011).
Additionally, the chemistry could be more complicated with additional
destruction routes missing from the model. Lastly, there could be a problem
with the ACE-FTS retrieval itself. The COClF line list used in the ACE-FTS
retrieval was taken from the ATMOS database and is described in the
literature as “very crude” (Perrin et al., 2011). At the time v3.0 data
were first released, this was the best line list available, but a new and
improved line list has subsequently been generated (Perrin et al., 2011), in
which the band intensities are taken from quantum-mechanical calculations.
ACE-FTS COClF retrievals for a handful of occultations have been carried out
using the new line list, but there is no improvement in the disagreement
with SLIMCAT.
It is expected that the sum of all fluorine source-gas VMRs (not including
those which have very long lifetimes compared with the period of
observations, e.g. CF4 and SF6) is anti-correlated with total
Fy VMR (HF + 2COF2+ COClF) in a conservative way (i.e. the
total adds up to a constant). As the ACE-FTS does not measure every source
gas, and some minor species have known biases (Brown et al., 2011), we only
compare total Fy against the sum of the major source gases, taken as
CFC-11, CFC-12, and HCFC-22. The good agreement between plots for SLIMCAT and
ACE-FTS (see Fig. 6) confirms that the discrepancy in modelled and retrieved
COClF VMRs has a minimal effect on the overall agreement between model and
observation for HF.
Trends
Since HF has no chemical sink, with only minor losses arising from rainout in
the troposphere and photolysis in the mesosphere, and since the atmosphere
contains many long-lived fluorine source gases, the overall HF atmospheric
abundance has been increasing for many years and is expected to increase in
the foreseeable future. In this section trends in ACE-FTS, HALOE, GOZCARDS,
and SLIMCAT time series are quantified as a function of altitude and
latitude. A number of previous studies have quantified trends; for example, a
linear trend of 8.5 ± 1.0 % year-1 (1977 to 1986) (Zander et
al., 1987) and 0.48 ± 0.25 % year-1 (2000 to 2009) (Kohlhepp
et al., 2012) in total columns measured at Jungfraujoch (46.5∘ N
latitude, 8.0∘ E longitude) and 0.74 ± 0.2 % year-1
(between 30∘ S and 30∘ N) derived from ACE-FTS data for
2004 to 2010 (Brown et al., 2011).
The GOZCARDS and SLIMCAT HF time series for selected
altitude–latitude bin combinations. Observations are plotted between
October 1991 and September 2010.
Prior to the calculation of ACE-FTS, HALOE, and SLIMCAT trends, we derived
time series as a function of altitude (on the ACE-FTS grid) and latitude (in
10∘ bins). Figure 7 illustrates the ACE-FTS, HALOE, and SLIMCAT time
series for HF between 1991 and 2013 at selected altitudes for six of the
latitude bins; for ease of viewing, error bars are not shown. The annual
cycle is clearly visible in each time series, a result of the seasonality of
the main “source”, COF2 (Harrison et al., 2014), and careful
inspection of Fig. 7 reveals that as expected the phase of this cycle is
opposite in each hemisphere. The amplitude is largest at high southern
latitudes (note the maxima at 29.5 km for the 60–70∘ S plot) due
to the descent of HF-rich air in southern winter polar vortices. Note also
evidence of the quasi-biennial oscillation signal in the tropical
plots.
Overall the agreement between SLIMCAT and observations presented in Fig. 7 is
good, but obvious biases are present. In Sect. 5 it was discussed that
ACE v3.0 is biased high relative to HALOE by ∼ 10 %. HALOE VMRs are
biased low relative to SLIMCAT, generally by between ∼ 5 and 15 %,
although SLIMCAT is biased low relative to HALOE by up to ∼ 20 %
between ∼ 20 and 30 km in the 0–30∘ N region. Additionally,
there is a discrepancy in the observed and calculated annual cycle structure
over the tropics, e.g. 10–20∘ N at 34.5 km. In terms of bias,
ACE-FTS v3.0/3.5 data generally agree with SLIMCAT to within ±5 %,
except over much of the lower stratosphere (below 30 km) where SLIMCAT is
biased low by at least ∼ 5–15 %, peaking at ∼ 20 % in
the 0–30∘ N region and ∼ 25–35 % at the southern and
northern high latitudes (poleward of 50∘).
Figure 8 illustrates the GOZCARDS and SLIMCAT time series for HF plotted in
the same manner as Fig. 7. Recall that the GOZCARDS HF data product is a
merging of the HALOE v19 and ACE-FTS v2.2 HF datasets, with the relative bias
between the datasets removed. The agreement between SLIMCAT and the HALOE
component of GOZCARDS above ∼ 30 km is reasonably good; however, at
lower altitudes there are several regions in which the low bias of SLIMCAT is
significantly larger than presented in Fig. 7, in particular below 20 km
near the South Pole and between ∼ 20 and 30 km in the Northern
Hemisphere, where the bias peaks at ∼ 35 % in the 0–30∘ N
region. The ACE-FTS component of GOZCARDS generally agrees with SLIMCAT to
within ±5 % above 30 km at the tropics and above 25 km in the polar
regions. At altitudes lower than these SLIMCAT is biased low, for example by
∼ 5–10 % at latitudes above 50∘ N and up to 25 %
lower between 20 and 30 km in the 0–30∘ N region.
The GOZCARDS merging process for HF relied only on the relative bias between
the HALOE v19 HF and ACE-FTS v2.2 HF datasets. In this study, it is not
possible to comment on systematic or absolute biases. However, regardless of
the absolute biases of the various datasets, it is clear that SLIMCAT tends
to consistently underestimate HF VMRs at low altitudes (below 30 km at the
tropics and 25 km at the poles) relative to those at higher altitudes.
Trends in the growth of HF (% year-1; January 2004
to December 2012) for ACE-FTS and SLIMCAT as a function of latitude and
altitude. A full discussion of these trends is provided in the text.
HF trends for the ACE-FTS, HALOE, and SLIMCAT time series (trends were not
considered for GOZCARDS as this is a merged dataset directly related in a
multiplicative fashion to the original datasets) at each altitude within
each latitude bin have been calculated for three time periods from monthly
percentage anomalies in HF zonal means, Cz,θ(n), defined as
Cz,θ(n)=100VMRz,θ(n)-∑m=112δnmVMR‾z,θ(m)∑m=112δnmVMR‾z,θ(m),
where n is a running index from month zero to month n-1, VMRz,θ(n) is the corresponding mixing ratio at altitude z and latitude θ, VMR‾z,θ(m) is the average of all zonal means
for each of the 12 months, m, and δnm, although not used in
its strict mathematical sense, is 1 when index n corresponds to one of
the months m and is 0 otherwise (Harrison et al., 2014). Such an
approach essentially removes the annual cycle and the effect of biases in
VMRs; the trend, in units of % year-1, is simply equated to the
“slope” of the linear regression between Cz,θ(n) and the
dependent variable n/12. The inclusion of additional terms such as the
annual cycle and its harmonics resulted in no additional improvement in the
regression. The three time periods considered are January 2004 to
December 2012 (ACE-FTS, SLIMCAT), October 1991 to December 1997 (HALOE,
SLIMCAT), and January 1998 to November 2005 (HALOE, SLIMCAT). The HALOE time
series was split into two periods for which HF growth could be modelled
linearly. Errors have been explicitly treated in the linear regression of the
ACE and HALOE data, but not the SLIMCAT outputs.
Figure 9 presents the trends in the growth of HF (percent per year)
(January 2004 to December 2012) for ACE and SLIMCAT as a function of latitude
and altitude, up to the top of the ACE-FTS retrieval range. The ACE-FTS plot
in Fig. 9 indicates that between 2004 and 2012, HF has increased most rapidly
(2–3 % year-1) at altitudes below ∼ 25 km in the Northern
Hemisphere and at ∼ 35 km near the Equator. Similarly, HF has
decreased most rapidly in the Southern Hemisphere below ∼ 35 km and in
the Northern Hemisphere between ∼ 30 and 35 km. The SLIMCAT plot in
the second panel contains a number of features which agree well with those in
the ACE plot. In particular, note the region of negative trends in the
Southern Hemisphere below ∼ 30–35 km, peaking at -3.5 to
-4.0 % year-1, the region of high positive trends in the Northern
Hemisphere below ∼ 30 km, peaking at 4.5–5.0 % year-1, the
small region of positive trend at ∼ 35 km near the Equator, peaking at
2.0–2.5 % year-1, and the slightly larger region of negative trend
at ∼ 30–40 km at 0 to 30∘ N, peaking at -1.0 to
-1.5 % year-1. However, the magnitudes of the SLIMCAT trends in
the lower stratosphere are biased high compared with the ACE measurements.
An additional SLIMCAT run has been performed with dynamics arbitrarily
annually repeating those for the year 2000; results from this run give a
“clean” HF signal without the complication of changes in stratospheric
dynamics. Results from this run are included in the times series plots in
Fig. 7; the annual repetition in dynamical structure reveals a clearer signal
of the long-term chemical changes. “Clean” HF trends for 2004–2012
calculated in the same manner as before are plotted in the lowest panel of
Fig. 9, revealing trends distributed relatively uniformly throughout the
stratosphere with values between 0 and 1 % year-1. This indicates
that the variations in trends observed for the full SLIMCAT run result from
changes in stratospheric dynamics over the observation period. The
information on stratospheric circulation is provided solely by the analyses
used to force the SLIMCAT calculations. Similar changes due to stratospheric
dynamics were observed for COF2 (Harrison et al., 2014). Moreover,
Ploeger et al. (2015) used a Lagrangian chemical transport model, also forced
by ECMWF ERA-Interim reanalyses, to look at variations in stratospheric
age-of-air (AoA) over the period 1988–2013. They compared their model
results with estimates derived from MIPAS satellite observations for
2002–2012. During the period of MIPAS observations they found that
stratospheric AoA decreased in the lower stratosphere but showed
interhemispheric differences in the trend above about 20 km. Also, despite
the ongoing monotonic decrease of near-surface chlorine source gases, recent
ground-based and satellite remote-sensing measurements have shown a
significant increase in hydrogen chloride (HCl), the main stratospheric
chlorine reservoir, in the lower stratosphere of the Northern Hemisphere
between 2007 and 2011 (Mahieu et al., 2014). By comparison to similar SLIMCAT
simulations as used here, this trend “anomaly” was attributed to multiyear
variability in the stratospheric circulation and dynamics.
Together, the studies discussed above paint a consistent picture whereby
variability in stratospheric transport, which varies with altitude and
hemisphere, significantly modifies the observed trends in long-lived
tracers. This variability seems to be well captured by reanalysis products
such as ERA-Interim. Even if these tracers have monotonic VMR trends in the
troposphere, this dynamical variability can lead to complicated behaviour in
the stratosphere and must therefore be accounted for when using observations
to determine underlying chemical trends. A detailed analysis of the changing
stratospheric dynamics that are responsible for the observed trends in HF
and other species is beyond the scope of this work and would require a
coupled chemical–dynamical model.
Trends have similarly been derived for the two HALOE periods. HALOE plots
corresponding to the ACE-FTS plots in Fig. 9 can be found in Figs. 10
(1991–1997) and 11 (1998–2005). The HALOE trends in Fig. 10 peaking in the
Northern Hemisphere between 0 and 40∘ N broadly agree with those
calculated by SLIMCAT in the same region, but SLIMCAT calculates smaller
trends at the lowest altitudes and generally underestimates the trends in the
Southern Hemisphere. The differences between the full and fixed-dynamics
SLIMCAT runs show the impact of dynamical variability; the fixed-dynamics run
provides a clean chemical signal. Of the three periods considered, the
comparison between 1991 and 1997 HALOE trends and those calculated from SLIMCAT
is the poorest. The north–south asymmetry in trends for the full SLIMCAT
calculation, which does not agree with observations, must be due to dynamical
variability in the model, with the dynamics imposed solely by the ECMWF
analyses. Over the measurement period, the quality of these analyses may vary
depending on the available datasets used for the assimilation, but it is very
difficult to test how realistic the stratospheric transport is. There are
only a handful of other height-resolved datasets that test this aspect of the
stratospheric circulation, e.g. ozone (Dhomse et al., 2015). As evidenced by
Fig. 11, however, the HALOE trends for 1998–2005 agree better with SLIMCAT
than for the 1991–1997 period, with “background” trends generally between
0.5 and 1.5 % year-1. In fact, there is very little variability
over the majority of the plotted range.
Trends (% year-1) derived from the HALOE v19 and ACE-FTS v3.0/v3.5
HF observations.
Trends in the growth of HF (% year-1; October 1991
to December 1997) for HALOE and SLIMCAT as a function of latitude and
altitude. A full discussion of these trends is provided in the text.
Overall global trends in HF, weighted at each altitude and latitude by
cos2(latitude∘) and the average VMR, have been calculated from
the three time series using errors determined from the linear regression;
these trends are listed in Table 5. The observed HF trends reveal a
substantial slowing down in the rate of increase of HF by ∼ 90 %
from the mid-1990s over the next 15 years, namely from 4.97 ± 0.12
(1991–1997) to 1.12 ± 0.08 (1998–2005) to
0.52 ± 0.03 % year-1 (2004–2012). In addition to direct
stratospheric ozone recovery (e.g. Chipperfield et al., 2015), this marked
decline in the growth rate of HF is a particularly important marker for the
success of the Montreal Protocol and should drop even further once HCFC-22
is phased out in developing countries over the coming years. Global trends
calculated by SLIMCAT for the HALOE (1998–2005) and ACE-FTS (2004–2012)
time series, 1.10 and 0.48 % year-1, respectively, agree very well
with observations; however, for the 1991–1997 HALOE period the model produces
a value ∼ 20 % lower (4.01 % year-1). Again, the reason
for this is not completely clear but is likely related to the ECMWF analysis
used to drive the dynamics in the SLIMCAT calculation.
Trends in the growth of HF (% year-1; January 1998
to November 2005) for HALOE and SLIMCAT as a function of latitude and
altitude. A full discussion of these trends is provided in the text.
Conclusions
HF is the most abundant fluorine reservoir in the
stratosphere with main sources arising from the atmospheric degradation of
CFC-12 (CCl2F2), CFC-11 (CCl3F), HCFC-22 (CHClF2), and
CFC-113 (CCl2FCClF2), ozone-depleting species whose emissions are
anthropogenic. Monitoring the growth of stratospheric HF is therefore an
important marker for the success of the Montreal Protocol.
Global distributions and trends of stratospheric HF have been determined from
ACE-FTS (2004–) and HALOE (1991–2005) data. Based on the overlap period
between datasets, ACE-FTS HF measurements are biased high by ∼ 10 %
relative to HALOE. The observations have been compared with the output of
SLIMCAT, a 3-D CTM, and the agreement is generally good,
although SLIMCAT tends to underestimate HF VMRs at low altitudes (below
30 km at the tropics and 25 km at the poles) relative to those at higher
altitudes.
The observed global HF trends reveal a substantial slowing down in the rate
of increase of HF since the 1990s: 4.97 ± 0.12
(1991–1997; HALOE), 1.12 ± 0.08 (1998–2005;
HALOE), and 0.52 ± 0.03 % year-1 (2004–2012; ACE-FTS),
indicating the effectiveness of the Montreal Protocol in phasing out the
principal precursor species. For the same periods, SLIMCAT calculates trends
of 4.01, 1.10, and 0.48 % year-1 respectively. The observations
also reveal variations in the HF trends with latitude and altitude; for
example between 2004 and 2012 HF actually decreased in the Southern
Hemisphere below ∼ 35 km. SLIMCAT calculations broadly agree with
these observations, most notably between 2004 and 2012. Such variations are
attributed to variability in stratospheric dynamics over the observation
period.
The ACE-FTS is the only satellite instrument currently making measurements
of HF, and it continues to operate with only minor loss in performance since
its launch. It will therefore be possible to extend the HF time series to
the present day and beyond, and subsequently extend the comparison with
SLIMCAT.
Data availability
ACE-FTS data were obtained from https://databace.scisat.ca/level2/ace_v3.0/ and https://databace.scisat.ca/level2/ace_v3.5/.
HALOE data were obtained from http://haloe.gats-inc.com/home/index.php.
The GOZCARDS data used in this work have not been officially released, but will become available via the GOZCARDS website (https://gozcards.jpl.nasa.gov/index.php) at a later
date.
Jeremy J. Harrison devised the study and performed the data analysis. Christopher D. Boone and Peter F. Bernath provided the ACE-FTS data. James Russell III provided
the HALOE HF data. Lucien Froidevaux and John Anderson provided the HF
GOZCARDS data. Martyn P. Chipperfield and Sandip S. Dhomse ran the SLIMCAT
model and provided additional explanation of the outputs. Jeremy J. Harrison
prepared the manuscript with contributions from Martyn P. Chipperfield and
the other co-authors.
Acknowledgements
The authors wish to thank the UK Natural Environment Research Council (NERC)
for supporting Jeremy J. Harrison through grant NE/I022663/1 and through the
National Centre for Earth Observation (NCEO). The ACE satellite mission is
funded primarily by the Canadian Space Agency (CSA). HALOE was funded by
NASA. Martyn P. Chipperfield and Sandip S. Dhomse thank Wuhu Feng (the
National Centre for Atmospheric Science; NCAS) for help with SLIMCAT.
Martyn P. Chipperfield is a Royal Society Wolfson Research Merit Award
holder. Work at the Jet Propulsion Laboratory was performed under contract
with the National Aeronautics and Space Administration (NASA). We thank the
ECMWF for providing the ERA-Interim reanalyses used by the SLIMCAT
model. Edited by: R. Müller
Reviewed by: two anonymous referees
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