Observing stratospheric ozone is essential to assess whether the Montreal
Protocol has succeeded in saving the ozone layer by banning ozone depleting
substances. Recent studies have reported positive trends, indicating that
ozone is recovering in the upper stratosphere at mid-latitudes, but the trend
magnitudes differ, and uncertainties are still high. Trends and their
uncertainties are influenced by factors such as instrumental drifts, sampling
patterns, discontinuities, biases, or short-term anomalies that may all mask
a potential ozone recovery. The present study investigates how anomalies,
temporal measurement sampling rates, and trend period lengths influence
resulting trends. We present an approach for handling suspicious anomalies in
trend estimations. For this, we analysed multiple ground-based stratospheric
ozone records in central Europe to identify anomalous periods in data from
the GROund-based Millimetre-wave Ozone Spectrometer (GROMOS) located in Bern,
Switzerland. The detected anomalies were then used to estimate ozone trends
from the GROMOS time series by considering the anomalous observations in the
regression. We compare our improved GROMOS trend estimate with results
derived from the other ground-based ozone records (lidars, ozonesondes, and
microwave radiometers), that are all part of the Network for the Detection of
Atmospheric Composition Change (NDACC). The data indicate positive trends of
1 % decade-1 to 3 % decade-1 at an altitude of about
39 km (3 hPa), providing a confirmation of ozone recovery in
the upper stratosphere in agreement with satellite observations. At lower
altitudes, the ground station data show inconsistent trend results, which
emphasize the importance of ongoing research on ozone trends in the lower
stratosphere. Our presented method of a combined analysis of ground station
data provides a useful approach to recognize and to reduce uncertainties in
stratospheric ozone trends by considering anomalies in the trend estimation.
We conclude that stratospheric trend estimations still need improvement and
that our approach provides a tool that can also be useful for other data
sets.
Introduction
After the large stratospheric ozone decrease due to ozone depleting
substances (ODSs) , signs of an
ozone recovery have been reported in recent years e.g.. Implementing the Montreal Protocol (1987) has succeeded in
reducing ODS emissions so that the total chlorine concentration has been decreasing
since 1997 . As a consequence, stratospheric ozone
concentrations over Antarctica have started to increase again, as shown by recent
studies .
Outside of the polar regions, however, differences in ozone recovery are
observed depending on altitude and latitude. The question as to whether ozone is recovering in the lower stratosphere is still controversial , whereas broad consensus exists that
stratospheric ozone has stopped declining in the upper stratosphere since
the end of the 1990s . Recently estimated trends for upper stratospheric ozone are
positive, but they are still different in magnitude and significance because
detecting a small trend is a difficult task. Many factors influence
stratospheric ozone such as variations in atmospheric dynamics, solar
irradiance, or volcanic aerosols and the increase of greenhouse gases
. Further, ozone trends might be masked by natural
variability.
Other important sources for trend uncertainties are instrumental drifts,
abrupt changes, biases, or sampling issues, e.g. due to instrumental
differences in sampling patterns or in vertical or temporal resolution.
Satellite drifts have been included in trend uncertainties in several studies
e.g.. Possible statistical methods to
consider abrupt changes in a time series are, for example, presented by
. Biases in ozone data sets can lead to important
differences in trend estimates, especially when they occur at the
beginning or the end of the considered trend period e.g..
The influence of non-uniform sampling patterns on trends was illustrated by
. Also showed that accounting for
temporal and spatial sampling biases and diurnal variability changes
satellite-based trends.
To account for several of the mentioned factors that influence trend
estimates, different approaches were published following the Scientific
Assessment of Ozone Depletion of the World Meteorological Organization (WMO)
in 2014 , with the aim to reduce uncertainties in trend
estimates. Drifts in single satellite data sets were, for example, considered
in the studies by or , whereas
used only stable satellite products with no or small
drifts. The study of summarizes recent trend
estimates using only updated satellite data sets with small drifts. The
drifts were mainly identified by and were not considered
in the trend estimates by or .
also used data from a large range of ground
stations, but possible biases or anomalies in these ground-based data were
not considered. The resulting ground-based trends consequently show some
important differences and were not used in their final merged trend profile.
used an advanced trend estimation method that considers
steps in satellite time series or biases due to measurement artefacts. Their
Bayesian method uses a priori information about the different satellite data
sets and results in an optimal merged ozone composite, but it has not yet
been applied to ground-based data.
The studies presented above agree on positive ozone trends in the upper
stratosphere with some differences in magnitude and show varying trends in
the middle and lower stratosphere. This agreement is more difficult to
observe in ground-based data sets, in which the data variability is larger due
to strong regional variability . Because of
this larger variability, considering instrumental biases or regional
anomalies is of special importance for trend estimations derived from
ground-based data. In addition to and
, several other studies presented ground-based trends of
stratospheric ozone profiles e.g., but biases in the data sets that might
influence the resulting trends have not been considered yet.
The present study proposes an approach to handle the problem of anomalous
observations in time series by considering the anomalies when estimating
trends. For this purpose, we present the updated data set of the ground-based
microwave radiometer GROMOS (GROund-based Millimetre-wave Ozone Spectrometer) located in
Bern, Switzerland. We determine its trends with a multilinear parametric
trend model by considering anomalies and
uncertainties in the time series, resulting in an improved trend estimate. To
identify such anomalies in the GROMOS data set, we compare the GROMOS data
with other ground-based data sets (lidars, ozonesondes, and microwave
radiometers) in central Europe (Sect. ). We define
anomalies as periods in which the data deviates from the other data sets. Before
applying our trend approach to the GROMOS time series
(Sect. ), we tested it with an artificial time series
(Sect. ). Not only anomalies in a time series
influence resulting trends, but also sampling patterns and the choice of the
trend period. We therefore present a short analysis of temporal sampling rate
and trend period length based on the GROMOS data set
(Sect. and ). Finally, we
compare the improved GROMOS trend with the trends from the other data sets
used (Sect. ).
Ozone data sets
The stratospheric ozone profile data used in the present study come from
different ground-based instruments that measure in central Europe
(Table ). They are all part of the Network for the
Detection of Atmospheric Composition Change . In addition, we
used data from the Microwave Limb Sounder (MLS) on board the Aura satellite
(Aura/MLS). All data from the different stations are compared to data from
the GROMOS radiometer located in Bern, Switzerland (46.95∘ N,
7.44∘ E; 574 m above sea level (a.s.l.)). The aerological
station (MeteoSwiss) in Payerne, Switzerland (46.8∘ N, 7.0∘ E;
491 m a.s.l.), is located 40 km south-west of Bern, which
ensures comparable stratospheric measurements. The Meteorological Observatory
Hohenpeissenberg (MOH; Germany; 47.8∘ N, 11.0∘ E;
980 m a.s.l.) is located 290 km north-east of Bern, and the
Observatory of Haute Provence (OHP; France; 43.9∘ N, 5.7∘ E;
650 m a.s.l.) lies 360 km south-west of Bern. Even if
stratospheric trace gases generally show small horizontal variability, the
distance between the different stations, especially between MOH and OHP, may
lead to some differences in measured ozone.
Information about measurement stations, instruments, and data used
in the present study.
StationInstrumentAltitude rangeMeasurement rateAnalysis period(mm/yyyy)Bern, SwitzerlandGROMOS31–0.8 hPa30 min to 1 h01/1995–12/201746.95 ∘ N, 7.44 ∘ E; 574 mPayerne, SwitzerlandSOMORA31–0.8 hPa30 min to 1 h01/2000–12/201746.8∘ N, 7.0∘ E; 491 mOzonesonde24–30 km13 profiles month-1a01/1995–12/2017Hohenpeissenberg, GermanyLidar24–42.3 km8 profiles month-1a01/1995–12/201747.8∘ N, 11.0∘ E; 980 mOzonesonde24–30 km10 profiles month-1a01/1995–12/2017Haute Provence, FranceLidar24–39.9 km11 profiles month-1a01/1995–12/201743.9∘ N, 5.7∘ E; 650 mOzonesonde24–30 km4 profiles month-1a01/1995–12/2017Aura satellite, above BernMLS31–0.8 hPaTwo overpasses day-108/2004–12/2017b46.95±1∘ N, 7.44±8∘ E
a Averaged number of profiles per month in the analysed period.
b For the trend calculations, data from January 2005 to December 2017 are
used.
Microwave radiometers
We use data from two microwave radiometers, both located in Switzerland. They
measure the 142 GHz line where ozone molecules emit microwave
radiation due to rotational transitions. The spectral line measured is
pressure-broadened and thus contains information about the vertical
distribution of ozone molecules. To obtain a vertical ozone profile, the
received radiative intensity is compared to the spectrum simulated by the
Atmospheric Radiative Transfer Simulator 2 (ARTS2; ).
By using an optimal estimation method according to , the
best estimate of the vertical profile of ozone volume mixing ratio is then
retrieved from the measured spectrum. This is done using the software tool
Qpack2, which together with ARTS2 provides an entire retrieval environment
.
The GROund-based Millimetre-wave Ozone Spectrometer (GROMOS) located in Bern
is the main focus of this study . GROMOS has
been measuring ozone spectra continuously since November 1994. Before
October 2009, the measurements were performed by means of a filter bench (FB)
with an integration time of 1 h. Since October 2009, a fast Fourier
transform spectrometer (FFTS) with an integration time of 30 min has
been used. An overlap measurement period of almost 2 years (October 2009 to
July 2011) was used to homogenize the FB data, by subtracting the mean bias
profile averaged over the whole overlap period
(FBmean-FFTSmean) from all FB
profiles . These homogenized ozone data are available on
the NDACC web page
(ftp://ftp.cpc.ncep.noaa.gov/ndacc/station/bern/hdf/mwave/, last
access: 28 March 2019). The FFTS retrieval used in the present study
(version 2021) uses variable errors in the a priori covariance matrix of
around 30 % in the stratosphere and 70 % in the mesosphere and a constant
measurement error of 0.8 K. The retrieved
profiles have a vertical resolution of ∼15 to 25 km in the
stratosphere. We concentrate in this study on the middle and upper
stratosphere between 31 hPa (≈24km) and
0.8 hPa (≈49km), where the retrieved ozone is
quasi-independent of the a priori information. This is assured by limiting
the altitude range to the altitudes where the area of the averaging kernels
(measurement response) is larger than 0.8, which means that more than 80 %
of the information comes from the observation rather than from the a priori
data . More information about the homogenization as well
as the parameters used in the retrieval can be found in .
Besides the described data harmonization to account for the instrument
upgrade, we performed some additional data corrections. Because the
stratospheric signal is weak in an opaque and humid troposphere, we discarded
measurements when the atmospheric transmittance was smaller than 0.3.
Excluding measurements in such a way should not result in a sampling bias
because tropospheric humidity is uncorrelated to stratospheric ozone. Also,
the data have been corrected for outliers at each pressure level by removing
values that exceed 4 times the standard deviation within a 3-day moving
median window. Profiles were excluded completely when more than 50 % of
their values were missing (e.g. due to outlier detection). Furthermore, we
omitted profiles in which the instrumental system temperature showed outliers
exceeding 4 times the standard deviation within a 30-day moving median
window.
The second microwave radiometer used in this study is the
Stratospheric Ozone MOnitoring RAdiometer
(SOMORA). It was built in 2000 as an update of the GROMOS radiometer and has
been located in Payerne since 2002. Some instrumental changes were performed
at the beginning of 2005 (front-end change) and in October 2010, when the
acousto-optical spectrometer of SOMORA was upgraded to an FFTS
. The data have been harmonized to account for the
spectrometer change. The instrument covers an altitude range from 25 to
60 km with a temporal resolution of 30 min to 1 h. In
this study, we consider SOMORA data at an altitude range between
18 hPa (≈27km) and 0.8 hPa
(≈49km). For more information about SOMORA, refer to
concerning the instrumental setup and
concerning the operational
version of the ozone retrievals used in the present study.
Lidars
We use data from two differential absorption lidar
(DIAL; ) instruments in Germany and France. The
instruments emit laser pulses at two different wavelengths, one of which is
absorbed by ozone molecules and the other which is not. Comparing the backscattered
signal at these two wavelengths provides information on the vertical ozone
distribution in the atmosphere. The lidars can only retrieve ozone profiles
during clear-sky nights due to scattering on cloud particles and the
interference with sunlight.
The lidar at the Meteorological Observatory Hohenpeissenberg (MOH) has been
operating since 1987, emitting laser pulses at 308 and 353 nm. On average, it retrieves eight
night profiles a month. In this study we limit the data to the altitude
range in which the measurement error averaged over the whole study period is
below 10 % (below 42 km or 2 hPa). The lower altitude limit
was set to the chosen limit of GROMOS at 31 hPa
(≈24km).
The Observatory of Haute Provence (OHP) operates a lidar that has been
measuring in its current setup since the end of 1993
. The lidar emits laser pulses at 308 nm
and 355 nm, as first described by . The instrument
measures on average 11 profiles per month. We use OHP lidar profiles below
40 km (≈2.7hPa) for which the averaged measurement
error remains below 10 %. As a lower altitude limit, we use 31 hPa
(≈24km) to be consistent with the GROMOS limits. More
detailed information about the lidars and ozonesondes used can be found, for
example, in and .
Ozonesondes
The three mentioned observatories at Payerne, MOH, and OHP also provide
weekly ozonesonde measurements. The ozonesonde measurements in Payerne are
usually performed three times a week at 11:00 UTC ,
resulting in 13 profiles per month on average. The meteorological balloon
carried a Brewer Mast sonde (BM; ) until September 2002,
which was then replaced by an electrochemical concentration cell (ECC;
). The profiles are normalized using concurrent total
column ozone from the Dobson spectrometer at Arosa, Switzerland (46.77∘ N,
9.7∘ E; 1850 m a.s.l.; ). If the Dobson
data are not available, forecast ozone column estimates based on GOME-2
(Global Ozone Monitoring Experiment–2) data are used
(http://www.temis.nl/uvradiation/nrt/uvindex.php, last access:
28 March 2019).
Ozone soundings at MOH are performed two to three times per week with a BM
sonde (on average 10 profiles per month). Three different radiosonde types
have been used since 1995, all carrying a BM ozonesonde, without major
changes in its performance since 1974 . The profiles
are normalized by on-site Dobson or Brewer spectrophotometers and, if not
available, by satellite data .
At OHP, ECC ozonesondes have been used since 1991 with several instrumental
changes . The data were normalized with total column ozone
measured by a Dobson spectrophotometer until 2007 and an ultraviolet–visible
SAOZ (Système d'Analyse par Observation Zénithale) spectrometer
afterwards . In our analysed period, four
profiles are available on average per month.
Ozonesonde data are limited to altitudes up to ∼30km, above
which the balloon usually bursts. Therefore, we used ozonesonde profiles only
below 30 km, which is a threshold value for Brewer Mast ozonesondes
with precision and accuracy below ±5 % . For
normalization, the correction factor (CF), which is the ratio of total column
ozone from the reference instrument to the total ozone from the sonde, has
been applied to all ozonesonde profiles. At all measurement stations, we
discarded profiles when their CF was larger than 1.2 or smaller than 0.8
. We further excluded profiles with extreme jumps
or constant ozone values, as well as profiles with constant or decreasing
altitude values.
Aura/MLS
The microwave limb sounder (MLS) on the Aura satellite, launched in mid-2004,
measures microwave emission from the Earth in five broad spectral bands
. It provides profiles of different trace gases in the
atmosphere, with a vertical resolution of ∼ 3 km. Stratospheric
ozone is retrieved by using the spectral band centred at 240 GHz. We
used ozone data from Aura/MLS version 4.2 above Bern with a spatial
coincidence of ±1 ∘ latitude and ±8 ∘
longitude, where the satellite passes twice a day (around 02:00 and
13:00 UTC). More information about the MLS instrument and the data product
can be found, e.g. in . We chose the Aura/MLS data for our
study because there are no drifts between 20 and 40 km.
Time series comparison
To identify potential anomalies in the GROMOS data, we compared the data with
the other described data sets in the time period from January 1995 to
December 2017, except for some instruments that cover a shorter time period
(Table ).
Comparison methodology
To compare GROMOS with the other instruments, the different data sets have
been processed to compare consistent quantities and have been smoothed to the GROMOS
grid. Taking relative differences between the data sets made it possible to
identify anomalous periods in the GROMOS time series.
Data processing
In this study we concentrate on the altitude range between 31 and
0.8 hPa, in which the a priori contribution to GROMOS profiles is low
(see Sect. ). We therefore limit all instrument data to this
altitude range and divide it into three parts. For convenience, they will be
referred to as the lower stratosphere between 31 and 13 hPa
(≈24 to 29 km), the middle stratosphere between 13 and
3 hPa (≈29 to 39 km), and the upper stratosphere between
3 and 0.8 hPa (≈39 to 49 km). The limits for the
upper stratosphere agree with the common definition
e.g., whereas the lower and middle
stratosphere defined here are usually referred to as the middle stratosphere in other studies.
Most of the instruments provide volume mixing ratios (VMRs) of ozone in parts
per million (ppm). In cases that the data were given in number density
(molecules cm-3), the VMR was calculated with the air pressure and
temperature provided by the same instrument for ozonesondes or co-located
ozone- or radiosonde data for lidars. For lidar measurements, these sonde
temperature and pressure profiles are completed above the balloon burst by
operational model data from the National Center for Environmental Prediction
(NCEP) at OHP and by lidar temperature measurements and extrapolated
radiosonde pressure data at MOH.
The GROMOS, SOMORA, and Aura/MLS profiles have a constant pressure grid, which
is not the case for the lidar and ozonesonde data. The lidar and ozonesonde
data were therefore linearly interpolated to a regular spaced altitude grid
of 100 m for the ozonesonde at OHP and Payerne and 300 m for
the lidars and the ozonesonde at MOH. The mean profile of the interpolated
pressure data then built the new pressure grid for the ozone data. These
interpolated lidar and ozonesonde data are used for the trend estimations.
For the direct comparison with GROMOS, the data were adapted to the GROMOS
grid, which is described in the next section
(Sect. ). Our figures generally show both
pressure and geometric altitude. The geometric altitude is approximated by
the mean altitude grid from GROMOS, which is
determined for each retrieved profile from operational model data of the European Centre for
Medium-Range Weather Forecasts (ECMWF).
GROMOS comparison and anomalies
The vertical resolution of GROMOS and SOMORA is usually coarser than for the
other instruments. When comparing profiles directly with GROMOS profiles, the
different vertical resolution of the instruments has to be considered.
Smoothing the profiles of the different instruments by GROMOS' averaging
kernels makes it possible to compare the profiles with GROMOS without biases
due to resolution or a priori information . The profiles
with higher vertical resolution than GROMOS were convolved by the averaging
kernel matrix according to , with
xconv=xa+AVK(xh-xa,h),
where xconv is the resulting convolved profile,
xa is the a priori profile used in the GROMOS retrieval,
xa,h is the same a priori profile but interpolated to the
grid of the highly resolved measurement, xh is the profile
of the highly resolved instrument, and AVK is the corresponding
averaging kernel matrix from GROMOS. The rows of the AVK have been
interpolated to the grid of the highly resolved instrument and scaled to
conserve the vertical sensitivity . The SOMORA profiles
have a similar vertical resolution as profiles from GROMOS and were thus not
convolved because this would require a more advanced comparison method as
proposed by or . GROMOS and SOMORA
have a higher temporal resolution than the other instruments. For SOMORA,
only profiles coincident in time with GROMOS have been selected. For the
other instruments, a mean of GROMOS data at the time of the corresponding
measurement was used, with a time coincidence of ±30min. Only
for the lidars were GROMOS data averaged over the whole lidar measurement
time (usually one night).
For comparison with GROMOS we computed relative differences between the
monthly mean values of the different data sets and the monthly mean of the
coincident GROMOS profiles. The relative difference (RD) for a specific month
i has been calculated by subtracting the monthly ozone value of the data
set (Xi) from the corresponding GROMOS monthly mean (GRi), using the
GROMOS monthly mean as a reference:
RDi,X=(GRi-Xi)/GRi⋅100.
Based on the relative differences we identified periods in which GROMOS differs
from the other instruments. To identify these anomalies we used a debiased
relative difference (RDdebiased), given by
RDdebiased,i,X=RDi,X-RD‾X,
where RD‾X is the mean relative difference of GROMOS to
the data set X over the whole period. This made it possible to ignore a
potential constant offset of the instruments and to concentrate on periods
with temporally large differences to GROMOS. When this debiased relative
difference was larger than 10 % for at least three instruments, the
respective month was identified as an anomaly in the GROMOS data. Above
2 hPa, for which only SOMORA and Aura/MLS data are available, both data
sets need to have a debiased relative difference to GROMOS larger than 10 %
to be identified as an anomaly.
(a) Monthly means of ozone volume mixing ratio (VMR) measured by
GROMOS (Ground-based Millimetre-wave Ozone Spectrometer) at Bern from
January 1995 to December 2017. The white lines indicate months for which no measurements were
available due to instrumental issues. (b) Deviation from GROMOS monthly mean
climatology (1997 to 2017), smoothed by a moving median window of 3 months.
GROMOS time series
The monthly means of the GROMOS time series (Fig. a)
clearly depict the maximum of ozone VMR between 10 hPa and
5 hPa and the seasonal ozone variation, with increased spring–summer
ozone in the middle stratosphere and increased autumn–winter values in the
upper stratosphere . Figure b shows
GROMOS' relative deviations from the monthly climatology (monthly means over
the whole period 1995 to 2017). This ozone deviation is calculated by the
ratio of the deseasonalized monthly means (difference between each individual
monthly mean and the corresponding climatology of this month) and the monthly
climatology. We observe some periods in which GROMOS data deviate from their usual values, mostly distinguishable between the lower–middle stratosphere
and the upper stratosphere. In the lower–middle stratosphere we observe
negative anomalies (less ozone than usual) in 1995 to 1997 and 2005 to 2006
and positive anomalies (more ozone than usual) in 1998 to 2000, in 2014 to
2015, and in 2017. In the upper stratosphere the data show negative anomalies
in 2016 and positive anomalies in 2000 and 2002 to 2003. Strong but
short-term positive anomalies are visible in 1997 in the upper stratosphere
and at the beginning of 2014. The positive anomaly in the upper stratosphere
in 1997 is due to some missing data in November 1997 because of an
instrumental upgrade, leading to a larger monthly mean value than usual.
Besides this we did not detect any systematic instrumental issues in the
GROMOS data that could explain the anomalies. Therefore, we compare the
GROMOS data with the presented ground-based data sets, as well as with
Aura/MLS data, to check whether the observed anomalies are due to natural
variability or due to unexplained instrumental issues.
Monthly means of ozone VMR from the microwave radiometers GROMOS
(Bern) and SOMORA (Payerne), the lidars at the observatories of
Hohenpeissenberg (MOH) and Haute Provence (OHP), and the ozonesonde measurements
at MOH, OHP, and Payerne, as well as Aura/MLS data above Bern. The data have
been averaged over three altitude ranges.
Comparison of different data sets
We compared GROMOS with ground-based and Aura/MLS data and averaged them over
three altitude ranges (Fig. ). The different data sets
have been smoothed with the averaging kernels of GROMOS to make a direct
comparison possible, as described in Sect. . Due
to the similar vertical resolution of GROMOS and SOMORA, the SOMORA profiles
have not been smoothed by GROMOS' averaging kernels, despite differences
between their a priori data and averaging kernels. This might lead to larger
differences between GROMOS and SOMORA than between GROMOS and the other
instruments. To avoid an instrument not covering the full range of one
of the three altitude ranges, all ozonesonde data have been cut at
30 km (≈11.5hPa), all lidar data at 3 hPa
(≈39km), and all SOMORA data below 13 hPa
(≈29km) for this analysis. The different instrument time
series shown in Fig. only contain data that are
coincident with GROMOS measurements as described in
Sect. , whereas the GROMOS data shown here
represent the complete GROMOS time series with its high temporal sampling.
This might lead to some sampling differences that are not considered in this
figure.
The different data sets agree well, showing, however, periods in which some
instruments deviate more from GROMOS than others. In the upper stratosphere
(Fig. a), GROMOS and SOMORA agree well most of the time,
but GROMOS reports slightly less ozone than SOMORA and also smaller values
than Aura/MLS. A step change between SOMORA and GROMOS is visible in 2005,
which might be related to the SOMORA front-end change in 2005. In the middle
stratosphere (Fig. b), both lidars exceed the other
instrument data in the last years, starting in 2004 at OHP and in 2010 at
MOH. Similar deviations of the MOH and OHP lidars have also been observed by
, as can be seen in the latitudinal lidar averages of
their study.
Relative differences (RD) of monthly means between
GROMOS and coincident pairs of SOMORA, lidars (MOH, OHP), ozonesondes
(Payerne, MOH, OHP), and Aura/MLS, averaged over three altitude ranges. The
relative difference (RD) is given by (GR-X)/GR⋅100, where GR represents GROMOS monthly means
and X represents monthly means of the other data sets. The black lines show
the mean values of all RDs, and the grey shaded areas show its standard
deviation.
Differences between the data sets in the lower stratosphere can be better
seen in Fig. . The monthly relative
differences of time coincident pairs of GROMOS (GR) and the convolved data
set X are shown, with GROMOS data as reference values (Eq. ). The mean
relative difference of all instruments compared to GROMOS (black line in
Fig. ) generally lies within ∼±10 %.
However, there are some periods with larger deviations, in which GROMOS measures
less ozone than the other instruments (negative relative difference) in 1995
to 1997 and in 2006 in the lower stratosphere and in 2016 in the middle and
upper stratosphere. We further observe that the relative difference between
GROMOS and the OHP ozonesonde shows some important peaks in the last decade,
indicating that the sonde often measures more ozone than GROMOS. The
ozonesonde data seem to have some outlier profiles. When comparing the
monthly means of coincident pairs, these outliers are even more visible
because only a small number of OHP ozonesonde profiles are available per
month (only four profiles on average).
For a broader picture, the same relative differences to GROMOS are shown in
Fig. , but each panel represents an individual instrument,
and all altitude levels are shown. The anomalies for which at least three data
sets (or two above 2 hPa) deviate by more than 10 % from GROMOS (as
described in Sect. ) are shown in the lowest
panel in black. In addition to the negative anomalies observed already in the
other figures (e.g. in 2006 in the lower stratosphere and in 2016 in the
upper stratosphere), we also observe positive anomalies in the lower–middle
stratosphere in 2000 and 2014. The negative anomalies in 1995 to 1997 in the
lower stratosphere that we observed in Fig. were only
partly detected as anomalies with our anomaly criteria.
To summarize our comparison results, we observed some periods with anomalies
compared to GROMOS' climatology (Fig. ). Some of these
anomalies were also observed when comparing GROMOS to the different data sets
(Figs. , , and
). This implies that the source of the anomalies is
local variations in Bern or instrumental issues of GROMOS rather than broad
atmospheric variability. We can thus conclude that the observed negative
GROMOS anomalies in the lower stratosphere in 2006 and in the upper
stratosphere in 2016 are biases in the GROMOS time series. The same is the
case for the positive anomalies in 2000 and 2014 in the lower and middle
stratosphere and also for some summer months in 2015, 2016, and 2017. In
contrast to these confirmed anomalies, the GROMOS anomalies in the lower
stratosphere in 1995 to 1997 (negative) and 1998 and 1999 (positive) as
observed in Fig. are small when comparing to the other
instruments and are thus only confirmed for a few months by our anomaly
detection. The biased periods in the upper stratosphere in 2000 and 2002 to
2003 (positive anomalies) were not confirmed by comparing GROMOS to SOMORA
and might thus be real ozone variations. However, we have to keep in mind
that fewer instruments (only SOMORA and Aura/MLS) provide data for comparison
above 2 hPa, which makes the anomaly detection less robust at these
altitudes, especially prior to the start of Aura/MLS measurements in 2004.
Our results are consistent with those reported by . They
compared GROMOS with Aura/MLS data and also observed positive deviations of
GROMOS in the middle stratosphere in 2014 and 2015 as well as a negative
deviation in the upper stratosphere in 2016. found
similar anomalies in the GROMOS time series by comparing ground-based data
sets to several satellite products (see also ). Some of
our detected biased periods were also found by who
compared different ground-based instruments, for example, the GROMOS anomaly
in 2006. They attribute the observed biases to sampling differences but also
to irregularities in some data sets. In fact, our results confirm
irregularities in the GROMOS time series, which are probably due to
instrumental issues of GROMOS and not only due to sampling differences.
Ozone trend estimations
Ozone trends are estimated in the present study by using a multilinear
parametric trend model . By comparing the GROMOS data
to the other data sets as described above (Sect. ), we have
confirmed some anomalous periods in the GROMOS time series. To improve the
GROMOS trend estimates, we now use these detected anomalies and consider them
in the regression fit. In the following, the trend model
(Sect. ) will first be applied to an artificial time
series to test and illustrate the approach of considering anomalies in the
regression (Sect. ). It will then be applied on GROMOS
data (Sect. ) before comparing the resulting GROMOS
trends to trends from the other instruments
(Sect. ).
Trend model
To estimate stratospheric ozone trends, we applied the multilinear parametric
trend model from to the monthly means of all
individual data sets. The model fits the following regression function:
y(t)=a+b⋅t+c⋅QBO30hPa(t)+d⋅QBO50hPa(t)+e⋅F10.7(t)+f⋅MEI(t)4+∑n=14gn⋅sin2πtln+hn⋅cos2πtln,
where y(t) represents the estimated ozone time series, t is the monthly
time vector, and a to h are coefficients that are fitted in the trend
model. QBO30hPa and QBO50hPa are the
normalized Singapore winds at 30 and 50 hPa, used as indices of the
quasi-biennial oscillation (QBO, available at
http://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/singapore.dat,
last access: 28 March 2019). F10.7 is the solar flux at a wavelength of
10.7 cm used to represent the solar activity (measured in Ottawa and
Penticton, Canada; ). The El
Niño Southern Oscillation (ENSO) is considered by the Multivariate ENSO
Index (MEI), that combines six meteorological variables measured over the
tropical Pacific . The F10.7 data and the MEI data are
available via https://www.esrl.noaa.gov/psd/data/climateindices/list/
(last access: 28 March 2019). In addition to the described natural
oscillations, we used four periodic oscillations with different wavelengths
ln to account for annual (l1=12 months) and semi-annual
(l2=6 months) oscillation as well as two further overtones of the annual
cycle (l3=4 months and l4=3 months).
Relative differences (RDs) of monthly means between GROMOS and
SOMORA, lidars (MOH and OHP), ozonesondes (Payerne, MOH, OHP), and Aura/MLS.
The lowest panel shows the mean of all RDs. The black areas in the lowest
panel show periods in which at least three data sets (or two data sets above
2 hPa) have a debiased relative difference (RDdebiased) larger
than 10 %.
The strength of the model from used in our study is
that it can consider inhomogeneities in data sets, by considering a full
error covariance matrix (Sy) when reducing the cost
function (χ2). Inhomogeneous data can originate from changes in the
measurement system (e.g. changes in calibration standards or merging of data
sets with different instrumental modes), from irregularities in spatial or
temporal sampling, or from unknown instrumental issues. Such inhomogeneities
lead to groups of temporally correlated data errors, that can, if not
considered, change significance and slope of the estimated trend
. The study of presents an approach to
consider known or suspected inhomogeneities in the trend analysis. They
divide the data into multiple subsets which are assumed to be biased with
respect to each other and which are characterized by diagonal blocks in the
data covariance matrix. We use their method and code in our trend analyses to
account for inhomogeneities. The inhomogeneities are in our case anomalies in
some months that we identified as described in
Sect. .
The total uncertainty of the data set is represented by a full error
covariance matrix Sy that describes
covariances between the measurements in time for each pressure level. The covariance matrix is for
each instrument given by
Sy=Sinstr+Sautocorr+Sbias,
where Sinstr gives the monthly uncertainty estimates
for the instrument, Sautocorr accounts for residuals
autocorrelated in time which are caused by atmospheric variation not captured
by the trend model, and Sbias describes the bias
uncertainties when a bias is considered. The diagonal elements of
Sinstr are set to the monthly means of the measurement
uncertainties for each instrument. For lidar data this is on average 4 %
for the OHP lidar and 6 % at MOH between ∼20 and 40 km, with
smallest uncertainties in the middle stratosphere. For the ozonesonde, the
uncertainty is assumed to be 5 % for all ECC sondes and 10 % for BM
sondes . For SOMORA we use uncertainties composed
of smoothing and observational error, ranging from 1 % to 2 % in the middle
stratosphere and 2 % to 7 % in the upper stratosphere. The Aura/MLS
uncertainties used range between 2 % and 5 % throughout the stratosphere.
For GROMOS we use uncertainty estimates as described by ,
composed of the standard error (σ/DOF, with standard
deviation σ and degrees of freedom DOF) of the monthly means, an
instrumental uncertainty (measurement noise), and an estimated systematic
instrument uncertainty obtained from cross-comparison. The resulting
uncertainty values are approximately 5 % in the middle stratosphere, 6 %
to 8 % in the lower stratosphere, and 6 % to 7 % in the upper
stratosphere. The off-diagonal elements of Sinstr are
set to zero, assuming no error correlation between the measurements in time.
The additional covariance matrix Sautocorr, which is
added to Sinstr, is first also set to zero. In a
second iteration, autocorrelation coefficients are inferred from residuals of
the initial trend fit. The mean variance of Sautocorr
is scaled such that the χ2 divided by the degrees of freedom of the
trend fit with the new Sy becomes unity. The degrees of
freedom are the number of data points minus the number of fitted variables.
The latter are the number of the coefficients of the trend model plus the
number of relevant correlation terms inferred by the procedure described
above. This additional covariance matrix Sautocorr
represents contributions to the fit residuals which are not caused by data
errors but by phenomena that are not represented by the trend model.
Sautocorr is only considered if the initial normalized
χ2 is larger than unity, which is not the case if the assumed data
errors are larger than the fit residuals . The more
sophisticated uncertainty estimates that we use for GROMOS are larger than
the residuals in the first regression fit (χ2<1), which means that
the time correlated residuals (Sautocorr) are not
considered for the GROMOS trend. For all the other instruments, however,
correlated residuals are considered because we use simple instrumental
uncertainties that are usually smaller than the fitted residuals.
To account for the anomalies in the time series when estimating the trends we
adapted Sy in two steps. First, we increased the
uncertainties for months and altitudes for which anomalies were identified (using
the method described in Sect. ). For this
purpose, we set the diagonal elements of Sinstr for
the respective month i to a value obtained from the mean difference to all
instruments (RDdebiased,i) and the mean GROMOS ozone
value. Assuming, for example, that GROMOS deviates from all instruments on
average by 10 % in August 2014 at 10 hPa, the overall mean
August value at this altitude would be 7 ppm. In this case we would assume
an uncertainty of 0.7 ppm for this biased month at this altitude level. In a
second step, we account for biases in the data subsets in which anomalies were
detected. For this, a fully correlated block composed of the squared
estimated bias uncertainties is added to the part of
Sbias that is concerned by anomalies. For example, to
account for a bias in the summer months of 2014, a fixed bias uncertainty is
added to all variances and covariances of these months in
Sbias. Considering the bias in this way is
mathematically equivalent to treating the bias as an additional fit variable
that is fitted to the regression model with an optimal estimation method, as
shown by . The value chosen for the bias uncertainty
determines how much the bias is estimated from the data itself. For small
values, the bias will be close to the a priori value, which would be a bias
of zero; for large values it will be estimated completely from the given
data. The bias can thus be estimated from the data itself, which makes the
method more robust because it does not depend on an a priori choice of the
bias . In a sensitivity test we found that assuming an
uncertainty value of 5 ppm for the correlated block permits a
reliable fit, whereby the bias is fitted independently of the a priori zero
bias.
The described procedure for anomaly consideration is only applied to the
GROMOS time series, whereas the other trend estimates were not corrected for
anomalies. Our ozone trend estimates always start in January 1997, which is
the most likely turning point for ozone recovery due to the decrease of ODSs
, and all end in December 2017. Exceptions are the trends
from SOMORA and Aura/MLS. The SOMORA trend starts in January 2000 when the
instrument started to measure ozone. Aura/MLS covers the shortest trend
period, starting only in January 2005. The trends are always given in
% decade-1, which is determined at each altitude level from the
regression model output in ppm decade-1 by dividing it for each
data set by its ozone mean value of the whole period. We declare a trend to
be significantly different from zero at a 95 % confidence interval, as soon
as its absolute value exceeds twice its uncertainty. This statistically
inferred confidence interval is based on the assumed instrumental
uncertainties. Unknown drifts of the data sets, however, are not considered
in this claim.
Artificial time series analysis
The trend programme from can handle uncertainties in
a flexible way, which makes it possible to account for anomalies in a time
series, as described above (Sect. ). To investigate
how anomalies can be best considered in the trend programme, we first test
the programme with a simple artificial time series and then try to use the
specific features of the trend programme to immunize it against anomalies.
The artificial time series used for this purpose consists of monthly ozone
values from January 1997 to December 2017 and is given by
y(t)=a+b⋅t+g⋅sin2πtln+h⋅cos2πtln,
with the monthly time vector t, a constant ozone value a=7ppm,
and a trend of 0.1 ppm decade-1, i.e. b=0.1/120ppm
per month. We consider a simple seasonal oscillation with an amplitude
A=1ppm such that A2=g2+h2 (e.g. g=h=-0.5ppm) and a wavelength ln=12 months. The uncertainty
was assumed to be 0.1 ppm for each monthly ozone value, which was
considered in the diagonal elements of Sy. No noise was
superimposed to the data. This artificial time series (shown in
Fig. a) is later on referred to as case A. The
estimated trend for this simple time series corresponds quasi-perfectly to
the assumed time series' trend (0.1 ppm decade-1), which proves
that the trend programme works well. The residuals are of order 10-6 and
increase towards the start and end of the time series (Fig. 5b).
(a) Artificial ozone time series (composed of a simple
seasonal cycle and a linear trend) and its model fit and linear trend
estimation. (b) Trend model residuals (data - model fit).
To investigate how the trend programme reacts to anomalies in the time
series, we performed the following sensitivity study. We increased the
monthly ozone values in the summer months (June, July, August) of 2014, 2015, and 2017 by 5 % (≈0.4 ppm). Since we are interested in the net
effect of the anomalies, again no noise was superimposed on the test data.
The same error covariance matrix Sy as in case A has been
used. For this modified time series (case B), we observe a trend of
∼0.13ppm decade-1 instead of the expected
∼0.1ppm decade-1 (Fig.
and Table ). Assuming that a real time series
contains such suspicious anomalies due to, for example, instrumental issues,
they would distort the true trend.
(a) Ozone time series and linear trends for the same
artificial ozone time series as in Fig. (case A).
Anomalies were added to the time series in the summer months 2014, 2015, and
2017 in case B. Different corrections have been applied to account for those
anomalies in the trend fit, represented by cases C to E. (b)
Linear trend estimates for the time series without anomalies (case A) and the
time series with added anomalies, for which the anomalies were considered in
different ways (cases C, D, E) or not considered at all (case B). The error bars show 2 standard deviation (σ)
uncertainties.
Summary of the artificial ozone time series and the different model
parameters used to correct the trend estimation for artificially added
anomalies.
Parameters in the artificial time series Parameters in the trend programme CaseTrue trendAdded anomaliesMonthly uncert.aUncert. for anomaliesbBias uncert.cEstimated trendA0.1 ppm decade-1–0.1 ppm––0.100±0.010ppm decade-1B0.1 ppm decade-15 %0.1 ppm––0.133±0.010ppm decade-1C0.1 ppm decade-15 %0.1 ppm0.36 ppm–0.103±0.011ppm decade-1D0.1 ppm decade-15 %0.1 ppm–5 ppm0.100±0.011ppm decade-1E0.1 ppm decade-15 %0.1 ppm0.36 ppm5 ppm0.100±0.011ppm decade-1
a Uncertainty considered in the diagonal elements of the covariance matrix Sy.
b Uncertainty considered in the diagonal elements of Sy for months with anomalies.
c Bias uncertainty considered in the off-diagonal elements of
Sy for months with anomalies, set as a correlated block.
A simple way to handle such anomalies would be to omit anomalous data in the
time series. This, however, would increase trend uncertainties and lead to a
loss of important information. Therefore, we use the presented approach to
handle anomalous observations in the time series when estimating the trend.
To account for these anomalies in the trend estimation, we make use of the
fact that the user of the trend programme has several options to manipulate
the error covariance matrix Sy. In a first attempt we
decreased the weight of the anomalies in the time series by increasing their
uncertainties (diagonal elements of Sy) and set the
uncertainties of the affected summer months to 0.36 ppm (case C in
Fig. and
Table ). This uncertainty value corresponds to
5 % of the overall mean ozone value. The uncertainty of the months without
anomalies remained 0.1 ppm. No additional error correlations between
the anomaly-affected data points were considered. The impact of the anomaly
is already reduced from about 33 % to about 3 %. The estimated error of
the trend has slightly increased because, with this modified covariance matrix
which represents larger data errors, the data set contains less information.
In a next step, we added a correlated block to the covariance matrix for the
months containing anomalies and applied the Sy once
without (case D) and once with (case E) the increased diagonal elements of
Sy. Adding the correlated block to Sy
corresponds to an unknown bias of the data subset that is affected by
anomalies and leads to a free fit of the bias magnitude. This bias is
represented in Sy as a fully correlated block of
(5 ppm)2. It has an expectation value of zero and an uncertainty of
5 ppm. With this approach (cases D and E), the trend obtained from the
anomaly-affected data is almost identical to the trend obtained from the
original data (case A). This implies that the trend estimation has
successfully been immunized against the anomaly.
In summary, we found that the trend estimates based on anomaly-affected data
are largely improved by consideration of the anomalies in the covariance
matrix, while without this, a largely erroneous trend is found. For this
purpose it is not necessary to know the magnitude of the systematic anomaly.
It is only necessary to know which of the data points are affected. We
further found that the trend estimate is closer to its true value when higher
uncertainties are chosen (diagonal elements of Sy or
correlated block in Sy; not shown). This can be explained
by the fact that the additional uncertainties represented by
Sy allow the bias to vary as in an optimal estimation
scheme in which the bias is a fit variable itself . When
estimating the bias, the larger the bias uncertainty is, the less confidence
is accounted to the a priori knowledge about the bias (that would be a bias
of zero), and the bias is then determined directly from the data as if it
were an additionally fitted variable. Based on our test with the
artificial time series, we conclude that our method succeeds in handling
suspicious anomalies in a time series, leading to an estimated trend close to
the trend that would be expected without anomalies.
GROMOS trends
The GROMOS time series has been used for trend estimations in
, who found a significant positive trend in the upper
stratosphere. In recent years, GROMOS showed some anomalies leading to larger
trends than expected in the middle atmosphere, as, for example, shown by
or . These larger trends motivated
us to improve GROMOS trend estimates by accounting for the observed
anomalies. In the following, we present the trend profiles of the GROMOS time
series by considering the detected anomalies in the trend programme with the
different correction methods that were introduced in
Sect. . In a first step, we estimated the trend
without considering anomalies in the data (case I in
Fig. ). The uncertainties of the data that are used as
diagonal elements in Sy range between 5 % and 8 % and
are composed as proposed by (see
Sect. ). In a second step (case II), we increased the
uncertainties (diagonal elements of Sy) for the months and
altitudes that were detected as anomalies. The uncertainties for these
anomalous data have been set to a value obtained from the difference to the
other data sets (RDdebiased), as described in
Sect. . Finally, we considered a fully correlated
block for the periods in which anomalies were detected to fit a bias (case III).
The bias uncertainty was set to 5 ppm at all altitudes, which ensures
that the bias is fully estimated from the data. The diagonal elements of
Sy stayed the same as in case II.
GROMOS ozone trends from January 1997 to December 2017, without considering anomalies (case I),
considering anomalies in the diagonal elements of Sy (case II), and considering a correlated bias block for anomalies (case III).
The shaded areas show 2σ uncertainties, whereas the bold lines represent altitudes at which the trend profile is significantly different from
zero at a 95 % confidence interval (|trend|>2σ).
The trend profiles in Fig. report an uncorrected
GROMOS ozone trend (case I) of about 3 % decade-1 in the middle
stratosphere from 31 to 5 hPa, which decreases above to around
-4 % decade-1 at 0.8 hPa. Correcting the trend by increasing
the uncertainty for months with anomalies (case II) decreases the trend
slightly in the lower stratosphere, but the differences are small. Using a
correlated block in the covariance matrix to estimate the bias of each
anomaly in an optimized way (case III) decreases the trend by around
1 % decade-1 in the lower stratosphere. The trend profile of this optimized
trend estimation has a trend of 2.2 % decade-1 in the lower and middle
stratosphere and peaks at approximately 4 hPa with a trend of
2.7 % decade-1. The corrected trend profile is consistent with recent
satellite-based ozone trends e.g. in the middle
stratosphere. In the upper stratosphere it decreases again to -2.4 % decade-1 at 0.8 hPa.
GROMOS (GR) trends from January 1997 to December 2017 using its high
temporal resolution (normal data set with correction for anomalies) as well
as the temporal sampling rate of the different lidars (a) and ozonesondes
(b). The shaded areas show the 2σ uncertainties, and bold lines identify
trends that are significantly different from zero.
All these different GROMOS trend estimates, considering anomalies in
different ways, show a significant positive trend of ∼2.5 % decade-1 at around 4 hPa (≈37km) and a trend
decrease above. This agreement indicates that the trend at these altitudes is
only marginally affected by the identified anomalies and is rather robust. We
can thus conclude that the trend of around 2.5 % decade-1 is a sign for
an ozone recovery in the altitude range of 35 to 40 km. This result
is consistent with trends derived from merged satellite data sets as, for
example, found in , , , or
, even though the GROMOS trend peak is observed at
slightly lower altitudes. A possible reason for this difference in the trend
peak altitude might be related to the averaging kernels of the current GROMOS
retrieval version. We observe that after the instrument upgrade in 2009, the
averaging kernels peak at higher altitudes than expected, indicating that the
information is retrieved from slightly higher altitudes (∼2 to
4 km). It is therefore possible that the trend peak altitude does not
exactly correspond to the true peak altitude. The instrumental upgrade in
2009 led to a change in the averaging kernels. This change, however, should
not influence the trend estimates because the data have been harmonized (see
Sect. ) and thus corrected for such effects. The harmonization
also corrects for possible effects due to changes in the temporal resolution
(from 1 h to 30 min).
In the upper stratosphere (above 2 hPa), the GROMOS trend estimates
are mostly insignificant but negative. This is probably influenced by the
negative trend observed in the mesosphere. A negative ozone trend in the
mesosphere is consistent with theory because increased methane emissions lead
to an enhanced ozone loss cycle above 45 km. However,
this is not further investigated in the present study because of the small
mesospheric measurement response in the GROMOS filter bench data
(before 2009).
In the middle and lower stratosphere (below 5 hPa), using different
anomaly corrections results in largest trend differences, with trends ranging
from 2 % decade-1 to 3 % decade-1. This result suggests that
the GROMOS anomalies mostly affect these altitudes between 30 and
5 hPa. The corrected GROMOS trend (case III) is not significantly
different from zero below 23 hPa, but cases I (uncorrected) and II
(corrected by Sy) show significant trends. Compared to
other studies, the GROMOS trends in the lower stratosphere are slightly
larger than trends of most merged satellite data sets.
In summary, correcting the GROMOS trend with our anomaly approach leads to a
trend profile of ∼2 % decade-1 to 2.5 % decade-1 in
the lower and middle stratosphere. This is consistent with satellite-based
trends from recent studies in the middle stratosphere but is still larger
than most satellite trends in the lower stratosphere. The GROMOS trends are
almost not affected by anomalies at 4 hPa (≈37km),
suggesting a robust ozone recovery of 2.5 % decade-1 at this
altitude. At lower altitudes, trends are more affected by the detected
anomalies, and the corrected trend estimate shows a trend of
2 % decade-1. The larger uncertainties in the lower stratosphere, the
dependency on anomalies, and the insignificance of the corrected trend show
that this positive trend in the lower stratosphere is less robust than the
trend at higher altitudes.
Influence of temporal sampling on trends
Stratospheric ozone at northern mid-latitudes has a strong seasonal cycle of
∼16 % and a moderate diurnal cycle of 3 % to
6 % . The sampling rate of ozone data is thus
important for trend estimates because measurement dependencies on season or
time of the day might influence the resulting trends. An important
characteristic of microwave radiometers is their measurement continuity,
being able to measure during day and night as well as during almost all
weather conditions (except for an opaque atmosphere) and thus during all
seasons. Other ground-based instruments such as lidars are temporally more
restricted because they typically acquire data during clear-sky nights only.
Clear-sky situations are more frequent during high-pressure events which vary
with season and location . The lidar
measurements thus do not only depend on the daily cycle, but also on location
and season. The seasonal dependency for ozonesondes might be smaller, but
they are only launched during daytime.
Figure gives an example of how the measurement
sampling rate can influence resulting trends. The GROMOS time series is used
for these trend estimates, once using only measurements at the time of lidar
measurements and once only at the time of ozonesonde launches. The
differences to the trend that uses the complete GROMOS sampling are not
significant but still important, especially between 35 and 40 km and
in the lower stratosphere. Using the sampling of the MOH lidar leads to
larger trends (∼3 % decade-1) than using the normal sampling
(∼2 % decade-1) below 5 hPa. The OHP lidar sampling,
however, leads to smaller trends than the normal sampling above
13 hPa. This suggests that selecting different night measurements
within a month can lead to trend differences.
All three ozonesonde samplings result in a larger trend than normal or lidar
sampling above 5 hPa. Even if ozonesondes are not measuring at those
altitudes, the result shows that measuring with an ozonesonde sampling (e.g.
only at noon) might influence the trend at these altitudes. Our findings
suggest that the time of the measurement (day or night) or the number and the
timing of measurements within a month can influence the resulting trend
estimates. Results concerning the time dependences of trends based on SOMORA
data can be found in . We conclude that sampling
differences have to be kept in mind when comparing trend estimates from
instruments with different sampling rates or measurement times.
Influence of time period on trends
An important factor that influences trend estimates is the length and
starting year of the trend period. Several studies have shown that the choice
of start or end point affects the resulting trend substantially
. Further, the number of years in the trend
period is crucial for the trend estimate . We
investigate how the GROMOS trends change when the regression starts in
different years. For this, we average the GROMOS trends over three altitude
ranges and determine the trend for periods of different lengths, all ending
in December 2017 but starting in different years
(Fig. ). As expected, the uncertainties increase with
decreasing period length, and trends starting after 2010 are thus not even
shown. Consequently, trends become insignificant for short trend periods. In
the lower and middle stratosphere, more than 11 years is needed to detect a
significant positive trend (at a 95 % confidence level) in the GROMOS data,
whereas the 23 years considered is not enough to detect a significant trend
in the upper stratosphere (above 3 hPa). and
state that at least 20 to 30 years is needed to detect a
significant trend at mid-latitudes, but their results apply to total column
ozone, which can not directly be compared with our ozone profiles. In general
we observe that the magnitude of the trend estimates highly depends on the
starting year. Furthermore, the trends start to increase in 1997 (middle
stratosphere) or 1998 (lower stratosphere), probably due to the turning point
in ODSs. The later the trend period starts after this turning point, the
larger the trend estimate is, which has also been observed by
. Starting the trend, for example, in the year 2000, as is
done in other studies e.g., increases
the GROMOS trend by almost 2 % decade-1 compared to the trend that starts
in 1997. The trend magnitudes depend on the starting year of the regression,
which is controversial to the definition of a linear trend that does not
change with time. This illustrates that the true trend might not be linear
or that some interannual variations or anomalies are not captured by the
trend model. Nevertheless, our findings demonstrate that it is important to
consider the starting year and the trend period length when comparing trend
estimates from different instruments or different studies.
GROMOS trends averaged over three altitude ranges starting in
different years, all ending in December 2017. The error bars show the 2σ
uncertainties. Trend estimates that are not significantly different from zero
at a 95 % confidence interval are shown in grey.
Trend comparison
The GROMOS trend profile (corrected as described above) and trend profiles
for all instruments at the other measurement stations (uncorrected) are shown
in Fig. . The trend profiles agree on a positive trend in
the upper stratosphere, whereas they differ at lower altitudes. Due to the
given uncertainties, most of the trend profiles are not significantly
different from zero at a 95 % confidence interval. Only GROMOS, the lidars, and the
ozonesonde at OHP show significant trends in some parts of the
stratosphere (bold lines in Fig. ).
Ozone trends of different ground-based instruments in central Europe
and Aura/MLS (over Bern, Switzerland). The 2σ uncertainties are shown by
shaded areas. Bold lines indicate trends that are significantly different
from zero (at a 95 % confidence interval). The trends at Bern and MOH as well
as the Aura/MLS trend are shown in (a), whereas (b) shows the trends from
data sets at Payerne and OHP.
We observe that all instruments that measure in the upper stratosphere show a
trend maximum between ∼4 and ∼1.8hPa (between
∼37 and 43 km), which indicates that ozone is recovering at
these altitudes. The trend maxima range from ∼1 % decade-1 to
3 % decade-1, which is comparable with recent, mainly satellite-based
ozone trends for northern mid-latitudes e.g.. Only the lidar trend at MOH is
larger throughout the whole stratosphere, with ∼3 % decade-1
in the middle stratosphere and 4 % decade-1 to 5 % decade-1
between 5 and 2 hPa. observed similar trend results
for the MOH lidar, even if they consider 5 fewer years with a trend period
ranging from 1997 to 2012. found that lidar data at
MOH and OHP deviate from reference satellite data above 35 km
before 2003, with less ozone at MOH and more ozone at OHP compared to SAGE
satellite data (Stratospheric Aerosol and Gas Experiment;
). Opposite drifts are reported by
after 2002 compared to MIPAS satellite data (Michelson Interferometer for
Passive Atmospheric Sounding; ). Combining those drifts
might explain our large MOH trends and smaller OHP trends. The distance of
∼600km between the MOH and OHP stations might also explain
some differences between the lidar trends. Furthermore, our sampling results
show that the lidar sampling at MOH leads to a larger trend in the lower
stratosphere than using a continuous sampling, whereas OHP lidar sampling
leads to a lower trend in the middle stratosphere. The large lidar trend at
MOH and the comparable low OHP lidar trend might therefore also be partly
explained by the sampling rate of the lidars. The GROMOS trend peaks at
slightly lower altitudes than the trends of the other instruments. This
difference might be related to the averaging kernels of GROMOS, which
indicate that GROMOS retrieves information from higher altitudes than
expected (∼2km difference).
In the middle and lower stratosphere, at altitudes below 5 hPa
(≈36km), the estimated trends differ from each other. The
microwave radiometers and the MOH lidar report trends of 0 % decade-1
to 3 % decade-1, and also the ozonesonde at OHP confirms this
positive trend. However, the ozonesondes at MOH and Payerne as well as
Aura/MLS and the OHP lidar report a trend of around 0 % decade-1 to
-2 % decade-1 below 5 hPa. Some of these observed trend
differences can be explained by instrumental changes or differences in
processing algorithms and instrument setup. The discrepancy between
ozonesonde and lidar trends at OHP, for example, are possibly due to the
change of the pressure–temperature radiosonde manufacturer in 2007, which
resulted in a step change in bias between ozonesonde and lidar observations.
A thorough harmonization would be necessary to correct the trend for this
change. The SOMORA trend shows a positive peak at 30 km, which is
probably due to homogenization problems that are corrected in the new
retrieval version of SOMORA, which is, however, not yet used in our analyses
. Furthermore, we have shown that sampling rates
and starting years have an important effect on the resulting trend. The trend
period lengths differ between SOMORA, Aura/MLS, and the other data sets, which
might also partly explain differences in trend estimates. To explain the
remaining trend differences in the lower stratosphere, further corrections,
e.g. for anomalies, instrumental changes, or sampling rates, would be
necessary. In brief, trends in the lower stratosphere are not yet clear. For
some instruments, significant positive trends are reported, but for many
other instruments, trends are negative and mostly not significantly different
from zero. This result reflects the currently ongoing discussion about lower
stratospheric ozone trends e.g..
In summary, our ground-based instrument data agree that ozone is recovering
around 3 hPa (≈39km), with trends ranging between
1 % decade-1 and 3 % decade-1 for most data sets. In the
lower and middle stratosphere between 24 and 37 km (≈31 and
4 hPa), however, the trends disagree, suggesting that further
research is needed to explain the differences between ground-based trends in
the lower stratosphere.
Conclusions
Our study emphasizes that natural or instrumental anomalies in a time series
affect ground-based stratospheric ozone trends. We found that the ozone time
series from the GROMOS radiometer (Bern, Switzerland) shows some unexplained
anomalies. Accounting for these anomalies in the trend estimation can
substantially improve the resulting trends. We further compared different
ground-based ozone trend profiles and found an agreement on ozone recovery at
around 40 km over central Europe. At the same time, we observed trend
differences ranging between -2 % decade-1 and 3 % decade-1
at lower altitudes.
We compared the GROMOS time series with data from other ground-based
instruments in central Europe and found that they generally agree within ±10 %. Periods with larger discrepancies have been identified and confirmed
to be anomalies in the GROMOS time series. We did not find the origins of
these anomalies and assume that they are due to instrumental issues of
GROMOS. The identified anomalies have been considered in the GROMOS trend
estimations because they can distort the trend. By testing this approach
first on a theoretical time series and then with the real GROMOS data, we
have shown that identifying anomalies in a time series and considering them
in the trend analysis makes the resulting trend estimates more accurate. With
this method, we propose an approach of advanced trend analysis based on the
work of that may also be applied to other ground- or
satellite-based data sets to obtain more consistent trend results.
Comparing the GROMOS trend with other ground-based trends in central Europe
suggests that ozone is recovering in the upper stratosphere between around 4
and 1.8 hPa (≈37 and 43 km). This result confirms
recent, mainly satellite-based studies. At other altitudes, we have observed
contrasting trend estimates. We have shown that the observed differences can
partly be explained by different sampling rates and starting years. Other
reasons might be instrumental changes or nonconformity in measurement
techniques, instrumental systems, or processing approaches. Further, the
spatial distance between some stations might explain some trend differences
because different air masses can be measured, especially in winter when polar
air extends over parts of Europe. Accounting for anomalies in the different
data sets as proposed in the present study might be a first step to improve
trend estimates. Combined with further corrections, e.g. for sampling rates
or instrumental differences, this approach may help to decrease discrepancies
between trend estimates from different instruments. In many other studies,
the observed trend differences are less apparent because the ground-based
data are averaged over latitudinal bands e.g.. Nevertheless, it is important to be aware that ground-based
trend estimates differ considerably, especially in the lower stratosphere.
Exploring the origin of the differences and improving the trend profiles in a
similar way as we did for GROMOS may be an important further step on the way
to monitoring the development of the ozone layer. To summarize, we have shown
that anomalies in time series need to be considered when estimating trends.
Our ground-based results confirm that ozone is recovering in the upper
stratosphere above central Europe and emphasize the urgency to further
investigate lower stratospheric ozone changes. The presented approach to
improve trend estimates can help in this endeavour.
Data availability
All ground-based data used in this study are available at
ftp://ftp.cpc.ncep.noaa.gov/ndacc. The MLS data from the Aura satellite
are available at https://disc.gsfc.nasa.gov/datasets/ML2O3_V004/summary.
Author contributions
LB and KH designed the concept and the methodology. LB carried
out the analysis and prepared the manuscript. TvC provided the trend model.
All co-authors contributed to the manuscript preparation and the
interpretation of the results.
Competing interests
The authors declare that they have no competing
interests. Thomas von Clarmann is co-editor of Atmospheric Chemistry and
Physics, but he is not involved in the evaluation of this paper.
Acknowledgements
The authors thank the Aura/MLS team for providing the satellite data. This
study has been funded by the Swiss National Science Foundation, grant number
200021-165516.
Review statement
This paper was edited by Jens-Uwe Grooß and reviewed by
two anonymous referees.
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