Variations in stratospheric inorganic chlorine between 1991 and 2006
D. J. Lary,1,2 D. W. Waugh,3 A. R. Douglass,2 R. S. Stolarski,2 P. A. Newman,2
and H. Mussa4
Received 16 March 2007; revised 28 August 2007; accepted 21 September 2007; published 13 November 2007.
[1] A consistent time series of stratospheric inorganic
chlorine Cly from 1991 to present is formed using space-
borne observations together with neural networks. A neural
network is first used to account for inter-instrument biases
in HCl observations. A second neural network is used to
learn the abundance of Cly as a function of HCl and CH4,
and to form a time series using available HCl and CH4
measurements. The estimates of Cly are broadly consistent
with calculations based on tracer fractional releases and
previous estimates of stratospheric age of air. These new
estimates of Cly provide a critical test for global models,
which exhibit significant differences in predicted Cly and
ozone recovery. Citation: Lary, D. J., D. W. Waugh, A. R.
Douglass, R. S. Stolarski, P. A. Newman, and H. Mussa (2007),
Variations in stratospheric inorganic chlorine between 1991 and
2006, Geophys. Res. Lett., 34, L21811, doi:10.1029/
2007GL030053.
1. Introduction
[2] Knowledge of the distribution of inorganic chlorine
Cly in the stratosphere is needed to attribute changes in
stratospheric ozone to changes in halogens, and to assess the
realism of chemistry-climate models [Eyring et al., 2006;
Eyring et al., 2007]. However, there are limited direct
observations of Cly. Simultaneous measurements of the
major inorganic chlorine species are rare [Zander et al.,
1992; Gunson et al., 1994; Bonne et al., 2000; Nassar et al.,
2006]. In the upper stratosphere, Cly can be inferred from
HCl alone [e.g., Anderson et al., 2000; Froidevaux et al.,
2006b].
[3] Here we combine observations from several space-
borne instruments using neural networks [Lary and Mussa,
2004] to produce a time series for Cly. A neural network is
used to characterize differences among various HCl meas-
urements, and to perform an inter-instrument bias correc-
tion. Measurements from several different instruments are
used in this analysis. These instruments, together with
temporal coverage and measurement uncertainties, are listed
in Table 1. The HALOE uncertainties are only estimates of
random error and do not include any indications of overall
accuracy. All instruments provide measurements through
the depth of the stratosphere. A second neural network is
used to infer Cly from these corrected HCl measurements
and measurements of CH4.
[4] Sections 2 and 3 describe the HCl and Cly intercom-
parisons. Section 4 presents a summary.
2. HCl Intercomparison
[5] We first compare measurements of HCl from the
different instruments listed in Table 1. Comparisons are
made in equivalent PV latitude - potential temperature
coordinates [Schoeberl et al., 1989; Proffitt et al., 1989;
Lait et al., 1990; Douglass et al., 1990; Lary et al., 1995;
Schoeberl et al., 2000] to extend the effective latitudinal
coverage of the measurements and identify contemporane-
ous measurements in similar air masses.
[6] The Halogen Occultation Experiment (HALOE) pro-
vides the longest record of space based HCl observations.
Figure 1 compares HALOE HCl with HCl observations
from (1) the Atmospheric Trace Molecule Spectroscopy
Experiment (ATMOS), (2) the Atmospheric Chemistry
Experiment (ACE), and (3) the Microwave Limb Sounder
(MLS). In these plots each point is the median HCl
observation made by the instrument during each month
for 30 equivalent latitude bins from pole to pole and 25
potential temperature bins from the 300–2500 K potential
temperature surfaces.
[7] For each of these bins we only use data in the range
where the supplied quality flags show it suitable for
scientific use. For each bin, we characterize the median
observation uncertainty and the representativeness uncer-
tainty. The representativeness is a measure of the spatial
variability over the bin, in our case characterized by the
average deviation of the observations in the bin. The
average deviation is a measure of the width of the proba-
bility distribution of observations. Unlike the standard
deviation, the average deviation is not strongly influenced
by a few outliers. Each of these uncertainties are used later
in Figures 2 and 3.
[8] A consistent picture is seen in these plots: HALOE
HCl measurements are lower than those from the other
instruments. The slopes of the linear fits (relative scaling)
are 1.05 for the HALOE-ATMOS comparison, 1.09 for the
HALOE-MLS, and 1.18 for the HALOE-ACE. The offsets
are apparent at the 525 K isentropic surface and above.
Previous comparisons among HCl datasets reveal a similar
bias for HALOE [Russell et al., 1996; McHugh et al., 2005;
Froidevaux et al., 2006a]. ACE and MLS HCl measure-
ments are in much better agreement (Figure 1d). Note, all
measurements agree within the stated observational uncer-
tainties summarized in Table 1.
[9] To combine the above HCl measurements to form a
continuous time series of HCl (and then Cly) from 1991 to
2006 it is necessary to account for the biases between data
GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L21811, doi:10.1029/2007GL030053, 2007
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1Goddard Earth Sciences and Technology Center, University of
Maryland Baltimore County, Baltimore, Maryland, USA.
2Atmospheric Chemistry and Dynamics Branch, NASA Goddard Space
Flight Center, Greenbelt, Maryland, USA.
3Department of Earth and Planetary Sciences, Johns Hopkins
University, Baltimore, Maryland, USA.
4Department of Chemistry, University of Cambridge, Cambridge, UK.
Copyright 2007 by the American Geophysical Union.
0094-8276/07/2007GL030053$05.00
L21811 1 of 5

sets. A neural network is used to learn the mapping from
one set of measurements onto another as a function of
equivalent latitude and potential temperature [Lary and
Mussa, 2004]. We consider two cases. In one case ACE
HCl is taken as the reference and the HALOE and Aura HCl
observations are adjusted to agree with ACE HCl. In the
other case HALOE HCl is taken as the reference and the
Aura and ACE HCl observations are adjusted to agree with
HALOE HCl. In both cases we use equivalent latitude and
potential temperature to produce average profiles. The
purpose of the mapping is simply to learn the bias as a
function of location, not to imply which instrument is
correct.
[10] The precision of the correction using the neural
network mapping is of the order of
0.3 ppbv, as seen in
Figure 1e which shows the results when HALOE HCl
measurements have been mapped into ACE measurements.
The mapping has removed the bias between the measure-
ments and has also straightened out the ‘wiggles’ in
Figure 1c, i.e., the neural network has learned the equivalent
PV latitude and potential temperature dependence of the
bias between HALOE and MLS. The inter-instrument off-
sets are not constant in space or time, and are not a simple
function of Cly.
3. Inorganic Chlorine Cly
[11] To a first approximation Cly
HCl + ClONO2 +
ClO + HOCl [Brasseur and Solomon, 1987]. However,
observations of ClONO2, ClO and HOCl are much more
limited than those of HCl (for example, there is no ACE
ClO product available above 29.5 km). As shown in Table 1,
ClONO2 measurements have been made by CLAES (1991–
1993), ATMOS (1992–1994), CRISTA (1994, 1998), and
ACE (2004–present). ClO measurements have been made
by the UARS MLS (1991–1999), aircraft, MkIV, ACE, and
Aura MLS (2004–present). HOCl measurements have been
made by ACE and Aura MLS.
Table 1. Instruments and Constituents Used in Constructing the Cly Record From 1991–2006
a
Instrument Temporal Coverage Species References Median Observation Uncertainty
ACE v2.2 2004–2006 HCl, ClONO2 [Bernath et al., 2005] 8% (HCl), 30% (ClONO2)
ClO, and HOCl >100% (ClO), >100% (HOCl)
ATMOS 1991, 1993, 1994 HCl, ClONO2 [Zander et al., 1992] 8% (HCl), 60% (ClONO2)
Aura MLS v1 2004–2006 HCl, ClO and HOCl [Froidevaux et al., 2006a] 12% (HCl), 76% (ClO), >100% (HOCl)
CLAES v9 1991–1993 ClONO2 [Roche et al., 1993] >100%
CRISTA 1994, 1997 ClONO2 [Offermann et al., 1999] 61%
HALOE v19 1991–2005 HCl [Russell et al., 1993] 4%
UARS MLS v5 1991–1999 ClO [Waters et al., 1996] >100% (ClO)
aThe uncertainties given are the median values calculated for each level 2 measurement profile and its uncertainty (both in mixing ratio) for all the
observations made. The uncertainties are larger than usually quoted for MLS ClO because they reflect the single profile precision, which is improved by
temporal and/or spatial averaging. The HALOE uncertainties are only estimates of random error and do not include any indications of overall accuracy.
Figure 1. (a, b, c, d) Scatter plots of all contemporaneous observations of HCl made by HALOE, ATMOS, ACE, and
MLS Aura. In Figure s 1a, 1b, and 1c HALOE is shown on the x-axis. (e) As in Figure 1c except that it uses the neural
network ‘adjusted’ HALOE HCl values. (f) The validation scatter diagram of the neural network estimate of Cly
HCl +
ClONO2+ ClO + HOCl versus the actual Cly for a totally independent data sample not used in training the neural network.
L21811 LARY ET AL.: STRATOSPHERIC CHLORINE FROM 1991–2006 L21811
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[12] Because of the limited temporal coverage of
ClONO2 measurements it is not possible to form a contin-
uous time series of Cly by combining HCl, ClONO2, HOCl,
and ClO. However, it is possible to form a time series of Cly
using a neural network trained by the available Cly obser-
vations. There are sufficient observations of ClONO2, ClO
and HOCl from aircraft, ACE, ATMOS, CLAES, CRISTA,
MkIV and MLS to train a neural network to learn the Cly
abundance as a function of HCl and CH4, for each of which
there is a long, near-continuous, time series of measure-
ments. The resulting reconstruction reproduces an indepen-
dent validation dataset faithfully with a correlation
coefficient of 0.99, and provides a scatter diagram with a
slope very close to one for the observed Cly plotted against
the neural network inferred Cly, see Figure 1f.
[13] The inputs to the neural network that estimates Cly
are HCl, CH4, equivalent latitude and potential temperature.
HCl is used because it is continuously observed from the
launch of UARS to the present and is typically the major Cly
reservoir. CH4 is used because it is continuously observed
from the launch of UARS to the present and, as a long-lived
tracer, it is well correlated with Cly. Potential temperature
and equivalent latitude are used because the correlation
between long-lived tracers such as CH4 and Cly is a strong
function of altitude and a weak function of latitude [Lary
and Mussa, 2004]. When we do the training we randomly
split our training dataset into three portions of 80%, 10%
and 10%. The 80% is used to train the neural network
weights. This training is iterative and on each iteration we
evaluate the current RMS error of the neural network. The
RMS error is calculated by using the second 10% of the data
that was not used in the training. We use the RMS error and
the way it changes with training iteration (epoch) to
determine the convergence of our training. When the
training is complete, we use the final 10% as a validation
dataset. This 10% of the data was randomly chosen and not
used in either the training or RMS evaluation. We only use
the neural network if the validation scatter diagram, which
plots the actual data from validation portion against the
neural network estimate, yields a straight line graph with a
slope of 1. This is a stringent and independent validation.
The validation is global as the data was randomly selected
Figure 2. Cly average profiles between 30
and 60
N for
October 2005, estimated by neural network calibrated to
HALOE HCl (blue curve), estimated by neural network
calibrated to ACE HCl (green), or from ACE observations
of HCl, ClONO2, ClO, and HOCl (red crosses). In each
case, the shaded range represents the total uncertainty; it
includes the observational uncertainty, the representative-
ness uncertainty (the variability over the analysis grid cell),
and the neural network uncertainty. The vertical extent of
this plot was limited to below 1000 K (
35 km), as there is
no ACE v2.2 ClO data for the upper altitudes. In addition,
above
750 K (
25 km) ClO constitutes a larger fraction of
Cly (up to about 10%) and so the large uncertainties in ClO
have greater effect.
Figure 3. (a, b, c) October Cly time-series for the 525 K isentropic surface (
20 km) and the 800 K isentropic surface
(
30 km). In each case the dark shaded range represents the total uncertainty in our estimate of Cly. This total uncertainty
includes the observational uncertainty, the representativeness uncertainty (the variability over the analysis grid cell), the
inter-instrument bias in HCl, the uncertainty associated with the neural network inter-instrument correction, and the
uncertainty associated with the neural network inference of Cly from HCl and CH4. The inner light shading depicts
the uncertainty on Cly due to the inter-instrument bias in HCl alone. The upper limit of the light shaded range corresponds
to the estimate of Cly based on all the HCl observations calibrated by a neural network to agree with ACE v2.2 HCl. The
lower limit of the light shaded range corresponds to the estimate of Cly based on all the HCl observations calibrated to agree
with HALOE v19 HCl. Overlaid are lines showing the Cly based on age of air calculations [Newman et al., 2006]. To
minimize variations due to differing data, coverage months with less than 100 observations of HCl in the equivalent latitude
bin were left out of the time-series.
L21811 LARY ET AL.: STRATOSPHERIC CHLORINE FROM 1991–2006 L21811
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over all temporal and spatial data points available. Several
training strategies were examined, the one described includ-
ed the most species over the longest time period.
[14] Figure 2 shows how Cly profiles estimated by the
neural network agree directly with observed Cly for October
2005. In each case the shaded range represents the total
uncertainty associated with the Cly estimate. The HCl bias
between HALOE and ACE (the difference between the
green and blue lines) is a major uncertainty. To enable us
to compare these neural network fits to observations, the red
crosses with error bars show the observed Cly
HCl +
ClONO2 + ClO + HOCl based on the available ACE data,
and there is good agreement between these observations and
the neural network calibrated to ACE data (note ACE ClO is
not available above 30 km).
[15] The distribution of Cly is expected to change be-
tween 1991 and 2006 as the abundances of its source gases
have changed. Figure 3 shows the time-series of Cly for the
525 K isentropic surface (
20 km) and the 800 K isentropic
surface (
30 km), for three different equivalent latitudes.
The upper limit of each light shaded range corresponds to
the estimate of Cly for the neural network calibrated to agree
with ACE v2.2 HCl, and the lower limit to the estimate of
Cly for the neural network calibrated to agree with HALOE
v19 HCl. The dark shading in Figure 3 shows the total
uncertainty, the root mean square of the observational
uncertainty, the representativeness uncertainty (the variabil-
ity over the analysis grid cell PV-theta bin), the inter-
instrument bias in HCl, the uncertainty associated with the
neural network inter-instrument correction, and the uncer-
tainty associated with the neural network inference of Cly
from HCl and CH4. We estimate the uncertainty due to the
neural network fitting using the neural network validation
scatter diagram. This scatter diagram shows a cloud of finite
width about the 1:1 line. We use the width of the cloud as an
estimate of the uncertainty associated with the neural
network fitting.
[16] The variation in Cly estimates between the two cases
depends on latitude, altitude and season and is typically
0.4 ppbv at 800 K. There is a general tendency of Cly to
increase in the 1990s, peak around 2000, and then slowly
decrease. This is consistent with our expectations based on
the tropospheric abundance of chlorine containing source
gases. The Cly time-series shown in Figure 3 constitutes a
useful test for model simulations. The variation in simulated
Cly from the chemistry-climate models used in the recent
[World Meteorological Organization (WMO), 2006] report
is much greater than the above uncertainty in Cly. For
example, the simulated peak annual-mean Cly for north
mid-latitudes varies from 0.8 to 2.8 ppb [Eyring et al.,
2007]. We have represented this uncertainty by the light
shading.
[17] The estimates of Cly produced are broadly consistent
with calculations based on tracer fractional releases
[Newman et al., 2006] and previous estimates of strato-
spheric age of air. Observations show that at 20 km the
mean age increases from around 2 years in the tropics to
around 4 years at high latitudes (60
N), with a similar
gradient at 30 km but older ages by around 2 years [Waugh
and Hall, 2002]. The curves in Figure 3 show calculations
of Cly for a range of values of the mean age of air, and the
ages that are required to match the observed Cly are
consistent with the observations of the mean age.
4. Summary
[18] A consistent time series of stratospheric Cly from
1991 to present has been formed using available space-
borne observations. Here we used neural networks to inter-
calibrate HCl measurements from different instruments, and
to estimate Cly from observations of HCl and CH4. These
estimates of Cly peaked in the late 1990s and have begun to
decline as expected from tropospheric measurements of
source gases and troposphere to stratosphere transport
times. Furthermore, the estimates of Cly are consistent with
calculations based on tracer fractional releases and age of air
[Newman et al., 2006]. The Cly time-series formed here is
an important benchmark for models being used to simulate
the recovery of the ozone hole. Although there is uncer-
tainty in the estimates of Cly, primarily due to biases in HCl
measurements, this uncertainty is small compared with the
range of model predictions shown in the recent [WMO,
2006] report.
[19] Acknowledgments. It is a pleasure to acknowledge NASA for
research funding (Aura Validation and MAP), Lucien Froidevaux and the
Aura MLS team for their data, the ACE team, Peter Bernath, Chris Boone,
and Kaley Walker for their data, the HALOE team and Ellis Remsberg for
their data, and the ATMOS team for their data. The ACE mission is funded
primarily by the Canadian Space Agency.
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A. R. Douglass, P. A. Newman, and R. S. Stolarski, Atmospheric
Chemistry and Dynamics Branch, NASA Goddard Space Flight Center,
Greenbelt, MD 20771, USA.
D. J. Lary, Goddard Earth Sciences and Technology Center, University of
Maryland Baltimore County, Baltimore, MD 21228, USA. (david@
lary.umbc.edu)
H. Mussa, Department of Chemistry, University of Cambridge, Lensfield
Road, Cambridge CB2 1EW, UK.
D. W. Waugh, Department of Earth and Planetary Sciences, Johns
Hopkins University, 320 Olin Hall, 3400 N. Charles Street, Baltimore, MD
21218, USA.
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