In this paper, we present the MErged GRIdded Dataset of Ozone Profiles
(MEGRIDOP) in the stratosphere with a resolved longitudinal structure, which
is derived from data from six limb and occultation satellite instruments:
GOMOS, SCIAMACHY and MIPAS on Envisat, OSIRIS on Odin, OMPS on Suomi-NPP,
and MLS on Aura. The merged dataset was generated as a contribution to the
European Space Agency Climate Change Initiative Ozone project
(Ozone_cci). The period of this merged time series of ozone
profiles is from late 2001 until the end of 2018.
The monthly mean gridded ozone profile dataset is provided in the altitude
range from 10 to 50 km in bins of 10∘ latitude × 20∘
longitude. The merging is performed using deseasonalized anomalies. The
created MEGRIDOP dataset can be used for analyses that probe our
understanding of stratospheric chemistry and dynamics. To illustrate some
possible applications, we created a climatology of ozone profiles with
resolved longitudinal structure. We found zonal asymmetry in the
climatological ozone profiles at middle and high latitudes associated with
the polar vortex. At northern high latitudes, the amplitude of the seasonal
cycle also has a longitudinal dependence.
The MEGRIDOP dataset has also been used to evaluate regional
vertically resolved ozone trends in the stratosphere, including the polar
regions. It is found that stratospheric ozone trends exhibit longitudinal
structures at Northern Hemisphere middle and high latitudes, with enhanced
trends over Scandinavia and the Atlantic region. This agrees well with
previous analyses and might be due to changes in dynamical processes related
to the Brewer–Dobson circulation.
Introduction
Nowadays, the importance of protecting the ozone layer and monitoring its
recovery from the effect of ozone depleting substances is well recognized
(e.g., Petropavlovskikh et al., 2019;
WMO, 2014, 2018). Past analyses have demonstrated that ozone is recovering
in the upper stratosphere
(e.g.,
Arosio et al., 2019; Bourassa et al., 2014; Kyrölä et al., 2013;
Petropavlovskikh et al., 2019; Sofieva et al., 2017b; Steinbrecht et al.,
2017; WMO, 2018). The ozone recovery in the lower stratosphere has not yet been
observed, and lower stratospheric ozone trends are the subject of recent
controversial discussions
(Ball
et al., 2018, 2019; Chipperfield et al., 2018).
In the majority of studies of ozone profile trends using satellite
observations made in limb-viewing geometry, analyses are performed on zonal
mean data. This representation allows ozone trends to be estimated globally.
At the same time, such representation provides a sufficiently large amount
of experimental data in spatiotemporal bins (usually 10∘ latitude
and 1 month) to enable robust estimation of trends. This is especially
important for the period before 2001 when long data records are available
only from solar occultation instruments having relatively sparse data
coverage.
A recent study by Arosio et al. (2019) using the merged
SCIAMACHY-OMPS dataset has shown that ozone trends for the period from 2003–2018
have a significant dependence on longitude. Also, total ozone column trends
(WMO, 2018 and references therein) have a pronounced zonal
structure.
This paper is focused on a new longitudinally resolved merged dataset of
ozone profiles in the stratosphere based on several limb and occultation
instruments. This new merged dataset is a contribution to the European Space
Agency Climate Change Initiative ozone project (Ozone_cci).
It can be used in different applications, including the evaluation of
regional ozone trends in the stratosphere.
The paper is organized as follows. In Sect. 2, we briefly discuss the
satellite data used for creating the merged dataset. Section 3 is dedicated
to the methodological aspects of data merging. Examples of ozone
distributions are shown in Sect. 4. Section 5 is dedicated to regional
trend analysis. A discussion and summary (Sect. 6) conclude the paper.
Data
The MEGRIDOP dataset is a merged and gridded dataset generated using ozone
profiles retrieved from several limb and occultation instruments, viz. MIPAS
(Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY
(SCanning Imaging Spectrometer for Atmospheric CHartographY) and GOMOS
(Global Ozone Monitoring by occultation of Stars), all on Envisat, OSIRIS
(Optical Spectrograph and InfraRed Imaging System) on Odin, OMPS-LP (Ozone
Mapping and Profiles Suite – Limb Profiler) on Suomi-NPP, and MLS (Microwave
Limb Sounder) on Aura.
These instruments provide high-quality ozone profiles with a good vertical
resolution of 2–4 km and a relatively dense spatiotemporal coverage
(100–3500 ozone profiles per day with fairly uniform sampling in longitude).
The important information about the datasets is collected in Table 1. More
information about the datasets from the individual satellite instruments is
found in Petropavlovskikh et al. (2019),
Sofieva et al. (2017b) and references therein.
General information about the datasets.
Instrument/ satelliteLevel 2 processor, referencesYearsVertical range/retrieval coordinateLocal time of level 2 dataNumber ofprofiles per dayMIPAS/EnvisatKIT/IAA V7R_O3_240 (von Clarmann et al.,2003, 2009)2005–20126–70 km,altitude10:00 and22:00∼ 1000SCIAMACHY/EnvisatUBr v3.5 (Jia et al., 2015)2002–20128–65 km, altitude10:00∼ 1300GOMOS/EnvisatALGOM2s v1 (Kyrölä et al., 2010; Sofieva et al., 2017a)2002–201110–105 km, altitude22:00∼ 110OSIRIS/OdinUSask v5.10 (Bourassa et al., 2018; Degenstein et al., 2009)2001–present10–59 km, altitude06:00 and 18:00∼ 250OMLS-LP/SUOMI-NPPUSask 2D v 1.1.0 (Zawada et al., 2018)2012–present6–59 km, altitude13:30∼ 1600MLS/AuraNASA v4.2 (Livesey et al., 2013)2004–present261–0.02 hPa (∼ 8–75 km), pressure01:30 and 13:30∼ 3000
For all instruments except MLS, the original ozone profile retrievals are
performed on an altitude grid. GOMOS, OSIRIS, SCIAMACHY and OMPS-LP
provide number density ozone profiles; therefore this representation (number
density on an altitude grid) is used for the merged dataset. For MIPAS, the
retrievals are performed in a volume mixing ratio vs. altitude grid. The
conversion to number density profiles is performed using temperature
profiles retrieved by MIPAS and the pressure profiles provided with the
MIPAS ozone data; the latter are constructed from altitude and temperature
using one (z, p, T) data point from the ERA-Interim reanalysis (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim, last access: 22 April 2021;
Dee et al., 2011).
For MLS, retrievals are performed in a mixing ratio on a pressure grid.
Similarly to the conversion procedure of MIPAS data, we performed the
conversion to number density using the retrieved MLS temperatures, but for
altitude–pressure conversion, we used the ERA-Interim reanalysis data. Such a
conversion might introduce some uncertainty in the MLS data. For studies of
long-term changes, this uncertainty is associated with a potentially
imperfect representation of temperature trends in ERA-Interim, which might
influence ozone trends. However, since current stratospheric temperature
trends (after 2000) are small
(Maycock
et al., 2018; Steiner et al., 2020), this uncertainty is expected to be
small. The MLS ozone profiles data record is stable
(Hubert et al., 2016); therefore, including MLS data into
the merged dataset is advantageous, especially for the merging method
applied in our work (see also below).
For all the instruments, we use the ozone profiles from the updated
HARMonized dataset of Ozone profiles (HARMOZ_ALT) developed
in the ESA Ozone_cci project (Sofieva et al.,
2013), https://climate.esa.int/en/projects/ozone/ (last access: 22 April 2021). HARMOZ consists of the
original retrieved ozone profiles from each instrument, which are screened
for invalid data by the instrument experts and are presented on a vertical
grid (altitude-gridded profiles are used in our paper) and in a common
netCDF4 format. Detailed information about the original datasets can be
found in Sofieva et al. (2013), and references to the
corresponding publications are also collected in Table 1 of our paper.
Merging method
The method used for creating the MEGRIDOP dataset is similar to that used
for the creation of the merged SAGE-CCI-OMPS dataset (Sofieva
et al., 2017b). Below we describe and illustrate the merging process.
Gridded monthly means from individual instruments
First, gridded ozone profile data ρi(z,b,t) in each 10∘×20∘ latitude–longitude bin b and at altitude z were created for
each individual dataset i and each month t. The mean number density
profile in each spatiotemporal bin is ρi(z,b,t). For each
instrument, we required more than 10 measurements in each spatiotemporal
bin. The uncertainty of the averaged data σi(z,b,t) is approximated
by the standard error of the mean (see discussion in Toohey and
von Clarmann, 2013 on possible influence of correlations caused by orbital
sampling on the standard error of the mean).
The non-uniformity of the sampling pattern can be characterized by the
inhomogeneity measure, which is defined as the linear combination of two
classical inhomogeneity measures, asymmetry A and entropy E: H=12(A+(1-E)) (Sofieva
et al., 2014). The unitless inhomogeneity measure H ranges from 0 to 1 (the
more homogeneous, the smaller H is). For our application, we considered the
inhomogeneity in time (Htime) as the main contribution to sampling
uncertainty.
Examples of gridded datasets at 30 km altitude for individual satellite
instruments are shown in Figs. 1 and 2. All instruments show a similar
morphology, although biases between individual datasets exist. The coverage
is instrument-specific and to some extent time-dependent; the most complete
coverage is achieved by MIPAS and MLS. The spatial bins are covered rather
uniformly by the data. Examples of the inhomogeneity measure Htime are
presented in Fig. S1 in the Supplement. Htime is very close to zero for
the instruments with dense sampling (MIPAS, SCIAMACHY, MLS, OMPS). For
OSIRIS and GOMOS, H is usually below 0.1 (good homogeneity of the data) with
a few exceptions for some months and locations. In this work, the
inhomogeneity measure Htime is used for detection of spatial bins with
high inhomogeneity of data (see below).
Examples of gridded monthly mean ozone number density (cm-3)
at 30 km for individual satellite instruments in January 2008.
Examples of gridded monthly mean ozone number density (cm-3)
at 30 km for individual satellite instruments in January 2018.
Seasonal cycle and deseasonalized anomalies
For each instrument i, latitude–longitude bin b and altitude level z, deseasonalized anomalies are computed as:
Δi(z,b,t)=ρi(z,b,t)-ρm,i(z,b)ρm,i(z,b),
where ρi(z,b,t) is the monthly mean in this spatial bin and ρm,i(z,b) is the climatological mean value for the month m. In other
words, from each January we removed the mean January value, from each February the mean February value, and so on.
In our computations, we removed values for spatial bins with less than 10
profiles and inhomogeneity Htime larger than 0.9. For all instruments
except for OMPS, the seasonal cycle is estimated using the years from 2005–2011.
For OMPS, the seasonal cycle is evaluated using data from 2012–2018. Figure 3 illustrates the seasonal cycle at 40 km for all instruments except GOMOS,
as the GOMOS data do not cover all months for the considered spatial bins.
Although biases are visible, the overall behavior of the seasonal cycle is
similar for the different datasets. In the tropics (left panel), small
differences in seasonal cycle between two longitude regions, 0–20 and 120–140∘ E, are observed, while at mid-latitudes, all
satellite instruments show consistently different seasonal cycles in these two longitude regions.
Examples of seasonal cycles in the tropics (a) and NH upper
stratosphere (b) at 40 km. Solid lines: longitudes 0–20∘ E;
dashed lines: longitudes 120–140∘ E. In the tropics, a semi-annual
cycle is observed.
For two instruments – MIPAS and MLS – which measure during day and
night and thus provide data at all latitudes in all seasons, we compared
the relative amplitude of the seasonal cycle max(ρm)-min(ρm)mean(ρm) at several altitude levels
(Fig. 4). As seen from
Fig. 4, longitudinal structures in the relative
amplitude of the seasonal cycle are observed to be largest in the northern
middle and high latitudes, particularly in the middle and upper
stratosphere.
Relative amplitude of seasonal cycle at 25 km (a, d), 35 km (b, e) and 45 km (c, f) for MIPAS (a, b, c) and MLS (d, e, f).
The merging of individual datasets was performed on deseasonalized
anomalies. The main advantage of using deseasonalized anomalies is that
various biases between the individual datasets – e.g.,
instrumental-specific, or those due to the difference in local time – are
automatically removed. The deseasonalization also removes spatial sampling
biases if the sampling patterns do not change over time. Details of the
applied merging method are presented in the next section.
Merging the data
The merging method used for creating MEGRIDOP is similar to that used in
creating the merged SAGE-CCI-OMPS dataset (Sofieva et al.,
2017b). The deseasonalized anomalies of all instruments except OMPS are
aligned, as the seasonal cycle was estimated using the same period. First,
we offset the OMPS deseasonalized anomalies to the median of the
deseasonalized anomalies from all other instruments. These additive offsets
are computed using the data from years 2012–2018, and the offsetting
procedure is illustrated in Fig. 5. In this
figure, we selected a spatial bin where the effect of the offsetting is
clearly visible. In many other bins, the offsets are small or negligible. As
observed in Fig. 5 (and also below in
Fig. 6), the deseasonalized anomalies from
individual datasets are in good agreement.
Illustration of offsetting the OMPS deseasonalized anomalies. The
data are shown for altitude 35 km and the 0–10∘ N, 0–20∘ E bin.
After offsetting OMPS, the merged ozone profiles in each spatiotemporal bin
and at each altitude level is obtained from the median of the deseasonalized
anomalies corresponding to individual instruments:
Δmerged(z,b,t)=median(Δi(z,b,t)).
The advantage of using the median estimate is that the merged anomaly
follows the majority of the data and it is not very sensitive to
exclusion or addition of an individual data record in cases where there are
several (and consistent) anomaly datasets available. The sensitivity of the
dataset and the evaluated trends to the number of instruments was studied in
detail for the SAGE-CCI-OMPS dataset, which is created with the same merging
algorithm (Sofieva et al., 2017b), and this is also valid for MEGRIDOP.
The uncertainties of the merged deseasonalized anomalies are computed
similarly to those used for the merged SAGE-CCI-OMPS dataset
(Sofieva et al., 2017b). For each instrument, the uncertainty
of the deseasonalized anomalies, σΔi, is estimated via
Gaussian error propagation; it is given by
σΔi=Δiσi2ρi2+σm,i2ρm,i2,
where σi is the uncertainty of the gridded ozone profiles (see
Sect. 3.1) and σm,i is the uncertainty of the seasonal cycle
ρm,i, which can be estimated via propagation of random
uncertainties to the mean value:
σm,i2=1Nm2∑j=1Nmσi2(z,b,tj),
where Nm is the number of monthly mean values in a given month m
available from all years.
Analogously to Sofieva et al. (2017b), the uncertainties of
the merged deseasonalized anomalies are estimated as
σΔ,merged=minσΔ,jmed,1N∑j=1NσΔ,j2+1N2∑j=1NΔj-Δmerged2,
where σΔ,imed is the anomaly uncertainty of the
instrument corresponding to the median value. In cases where there are an even
number of measurements, the mean of two neighbors to the median is used.
Analogously to uncertainty estimates in the merged SAGE-CCI-OMPS dataset
(Sofieva et al., 2017b), the uncertainties given by Eq. (5) can be
interpreted as follows. If individual anomalies are significantly different,
the uncertainty of the merged anomaly is the uncertainty corresponding to
the median value. In cases where several instruments report a similar
anomaly (intersecting error bars), this provides more confidence in this
anomaly value, and the resulting uncertainty of the merged anomaly is
approximated by the second term in Eq. (5).
The deseasonalized anomalies from individual datasets are usually very close
to each other so that several values can be typically found within the
uncertainty interval of the merged anomaly Δmerged±σΔ,merged. This is similar to the approach taken with the
SAGE_CCI-OMPS dataset (Sofieva et al., 2017b, Fig. S8).
Examples of deseasonalized anomalies and their estimated uncertainties are
displayed in Figs. 6 and 7, respectively.
An example of deseasonalized anomalies (in %) for individual
instruments and the merged dataset in the spatial bin 0–10∘ N,
0–20∘ E.
An example of uncertainties in deseasonalized anomalies (in %)
for individual instruments and the merged dataset in the spatial bin
0–10∘ N, 0–20∘ E.
The average estimated uncertainty of the merged ozone is usually less than
2 % before 2012 and below 1 % after 2012. In the upper troposphere and the lower stratosphere (UTLS), uncertainties are
larger than in the stratosphere; they are typically in the range of 2 %–12 % before 2012 and 2 %–6 % after 2012.
The merged dataset and selected examples
The merged deseasonalized anomalies can be directly used for evaluation of
ozone trends in the stratosphere. The evaluation of regional ozone trends is
discussed in Sect. 5 of our paper. We also created a version of MEGRIDOP
in number density through restoration of the seasonal cycle. This was
achieved in a manner similar to that applied in creating the merged
SAGE-CCI-OMPS dataset (Sofieva et al., 2017b). The best
estimates of the amplitude and morphology of the seasonal cycle are provided
by MIPAS and MLS, as these two instruments provide global coverage in all
seasons. The ozone profiles from OSIRIS and MLS have the smallest biases
with respect to ozone soundings (Hubert et al., 2016). For
the seasonal cycle of the merged dataset, we computed the mean of MIPAS and
MLS seasonal cycles and offset it to the mean of OSIRIS and MLS values (this
offset does not depend on season). Using this procedure, the seasonal cycle in
the merged dataset has absolute values, which have the smallest biases with
respect to the ground-based instruments and a realistic amplitude. An
example of a number density MEGRIDOP dataset is shown in
Fig. 8.
An example of number density ozone profiles (in cm-3) for
individual instruments and the merged dataset in the spatial bin
0–10∘ N, 0–20∘ E.
The merged dataset allows us to provide a gridded climatology of ozone
profiles, i.e., the collection of ozone profiles categorized by calendar
month, latitude, longitude and altitude. Figure 9
shows these climatological ozone values for 4 months and at four
altitude levels. The polar projections of these distributions are presented
in the Supplement (Figs. S2 and S3). As observed in these figures, there
is zonal asymmetry associated with the polar vortex in both hemispheres. In
other locations, the ozone distributions are rather uniform in longitude.
Climatological ozone distributions (in DU km-1) for January, April,
July and October for selected altitude levels (15, 20, 30 and 40 km).
Evaluation of regional ozone trends
For evaluation of the regional ozone trends, we exploited the standard
approach of multiple linear regression and applied it to the deseasonalized
anomalies:
Δmerged(t)=at+b+q1QBO30(t)+q2QBO50(t)+sF10.7(t)+dENSO(t),
where we model the trend with a simple linear term, QBO30(t) and
QBO50(t) are the equatorial winds at 30 and 50 hPa, respectively
(http://www.cpc.ncep.noaa.gov/data/indices/, last access: 22 April 2021), F10.7(t) is
the monthly average solar 10.7 cm radio flux (https://www.ngdc.noaa.gov/stp/solar/flux.html, last access: 25 April 2021) and ENSO(t) is the 2 month lagged ENSO
proxy (https://psl.noaa.gov/enso/mei/, last access: 23 April 2021). The
evaluation of trends has been performed for each latitude–longitude bin and
for each altitude level separately. Autocorrelations are removed using the
Cochrane–Orcutt transformation (Cochrane and Orcutt,
1949).
In our analysis, we consider long-term trends over the years covered by
MEGRIDOP and approximate them by a linear function (which describes bulk
changes). However, real changes in the atmosphere can be non-linear
(Laine et al., 2014): if variations are analyzed on a shorter
timescale, they can be different from long-term trends (e.g.,
Arosio et al., 2019;
Chipperfield et al., 2018; Galytska et al., 2019; Strahan et al., 2020). We
selected the years after 2003 in order to avoid the influence of a major
sudden stratospheric warming in September 2002 on ozone trends at Southern
Hemisphere middle and high latitudes (see also the discussion below).
Ozone trends (expressed in percent per decade) estimated at several altitude
levels for the years from 2003–2018 are shown in Fig. 10.
Figure 11 displays the trends at these altitudes in
absolute units, DU km-1 decade-1). In Figs. 10 and 11, black stars indicate
the statistically significant trends, i.e., trends different from zero at a
95 % or greater confidence level. The morphology of ozone trends presented
in absolute and in relative units look similar. As shown in Figs. 10 and
11, statistically significant trends are observed in the upper stratosphere.
A longitudinal structure is clearly visible in the NH mid-latitude trends
above 40 km: the trends are significantly larger over Scandinavia and the northern Atlantic
Ocean (5 %–6 % decade-1) than over Siberia (∼ 1 % decade-1). The same feature was also observed by Arosio et al. (2019). Enhanced ozone trends over the mid-latitude Atlantic sector are seen
in both absolute and relative units, and also at lower altitudes (but the
ozone trends are not statistically significant below 40 km).
Ozone trends (% decade-1) in 2003–2018 for several
altitudes. Statistically significant trends are indicated by stars.
Same as Fig. 10, but for trends in DU km-1 decade-1.
We also compared the trends in late 2004–2018, the common measurement
period, using MEGRIDOP, only MLS data and the merged SCIAMACHY-OMPS dataset
by Arosio et al. (2019). We found that the spatial distributions of ozone
trends are similar for the considered datasets
(Fig. 12, top). The MEGRIDOP and pure MLS ozone
trends in 2004–2018 are similar (as expected, MLS data are used in
MEGRIDOP). SCIAMACHY-OMPS trends are somewhat larger, which might be related
to the OMPS drift (Kramarova et al., 2018), but within error
limits, and the morphology of ozone trends is similar. Specifically
interesting is a two-core structure of ozone trends in the NH polar region,
and this is seen nearly at all altitude levels
(Fig. 12, bottom) for all datasets.
(a, b, c) Ozone trends in late 2004–2018 (% decade-1) at 35 km and
(d, e, f) longitude–altitude cross section of the ozone trends at
∼ 65∘ N (the latitude is indicated by a dashed line
on the top panels). Ozone trends are estimated using the MEGRIDOP (a, d), MLS
(b, e) and SCIAMACHY-OMPS datasets (c, f). For the SCIAMACHY-OMPS
dataset, ozone trends in the Southern Atlantic Anomaly (SAA) region are not
shown because SCIAMACHY data are flagged in this region.
There are several analyses showing that the residual circulation has a
pronounced longitudinal two-core structure at Northern Hemisphere high and
middle latitudes (e.g.,
Demirhan Bari et al., 2013; Kozubek et al., 2015).
Kozubek et al. (2015) also performed a trend
analysis and showed a weakening of the two-core structure, which possibly
affects the ozone distribution in the region. Arosio et al. (2019) suggested
that this longitudinal structure in the NH mid-latitude ozone trends is due
to changes in dynamical processes related to the 3D structure of the Brewer–Dobson circulation. However, the origin of the longitudinal structure of
ozone trends requires more detailed investigations in the future, including simulations
with chemistry-transport models.
Statistically significant (at 95 % confidence level) positive trends (1 %–2 % decade-1) are also observed at SH mid-latitudes (∼ 40–50∘ S) at 25 km. This is in agreement with other
studies of zonally averaged ozone trends
(e.g., Arosio et al., 2019;
Petropavlovskikh et al., 2019; Sofieva et al., 2017b). In our analysis, there
is a zonal asymmetry with larger trends in the sector 50∘ W–10∘ E. At altitudes of 20–25 km, the trend patterns are different in
the Northern and Southern Hemispheres.
Comparisons of MEGRIDOP ozone trends at 35 km in Figs. 10 and 12 show
larger positive ozone trends in the tropics in the period from 2004–2018
compared to the period from 2003–2018. A pronounced sensitivity of tropical
ozone trends at ∼ 35 km to the selection of the period for
evaluation of ozone trends has been reported in several papers (e.g., Laine
et al., 2014; Arosio et al., 2019; Galytska et al., 2019). As a hypothesis,
this might be related to a decadal-scale ozone oscillation resulting from
changes in Brewer–Dobson Circulation.
In previous studies (e.g.,
Petropavlovskikh et al., 2019; Steinbrecht et al., 2017; WMO, 2018), ozone
trends have been evaluated at latitudes 60∘ S–60∘ N,
i.e., excluding polar regions. In this study, we have made an attempt to also
evaluate ozone trends in polar regions. The ozone trends in polar
projections are shown in the Supplement.
We found statistically significant positive trends in the NH polar middle
stratosphere (25–30 km). In the SH polar regions, the estimated ozone trends
are mostly positive, but they are not statistically significant. We found
that the estimated trends in the SH polar regions are sensitive to the
inclusion of 2002 data into the trend analysis. Quite exceptional (larger)
ozone values in 2002 due to a SH major sudden stratospheric warming result
in negative, although not statistically significant, ozone trends in the SH
polar stratosphere, as expected since 2002 is at the beginning of the time
period. If data from 2002 are excluded from the analysis, the estimated
trends over Antarctica are not sensitive to the selection of the starting
point for the trend analysis. This can be observed, for example, by
comparison of ozone trends at 35 km in Fig. 10 (trends for 2003–2018) and
Fig. 12 (trends for late 2004 to 2018).
Since natural variability is high in polar regions and the observational
period is relatively short, it is quite expected that a simple multiple
regression will lead to trend estimates that are not statistically
significant. Other methods for trend analysis in polar regions, such as
considering seasonal trends
(Solomon et al., 2016;
Szeląg et al., 2020; Galytska et al., 2019) can be explored in future
work. In addition, the relation of winter–spring trends with respect to the
position of the polar vortex would be an interesting subject in future
studies.
The satellite data quality typically degrades in the UTLS compared to higher
levels in the stratosphere. Our merging principle seems to be particularly
optimal for the UTLS datasets, as it automatically removes biases, which can
be significant in this altitude region. The very large natural ozone
variability results in MEGRIDOP trend estimates below 20 km being
not statistically significant in most locations.
Summary
In this paper, we presented the merged gridded dataset of ozone profiles
(MEGRIDOP), which combines ozone data from six limb-viewing satellite
instruments. The merged gridded ozone profiles are the monthly means in
10∘×20∘ latitude–longitude bins, and they cover
altitudes from 10 to 50 km. This dataset covers the years from 2001–2018 and will
be extended regularly in the future.
The merging was performed using aligned deseasonalized anomalies: the merged
dataset represents the median of the deseasonalized anomalies from the
individual instruments. The merged deseasonalized anomalies can be used
directly for evaluation of ozone trends. For other applications, the
MEGRIDOP is also available in the form of ozone number density profiles.
The MEGRIDOP dataset can be used in different analyses. As an illustration
of one of the possible applications, a climatology of ozone profiles with
resolved longitudinal structure has been created. We found zonal
asymmetry in the climatological ozone profiles at middle and high
latitudes associated with the polar vortex. At northern high latitudes, the
amplitude of the seasonal cycle also has a longitudinal dependence.
We evaluated regional ozone trends over the years from 2001–2018 using a multiple
linear regression method. Overall, the estimated trends agree well with the
trends derived from zonal mean ozone profiles. We found a zonal asymmetry in
the upper stratospheric ozone trends at middle and high latitudes in the
Northern Hemisphere: the trends are larger over Scandinavia
than over Siberia. This feature agrees well with previous analyses and might
be due to changes in dynamical processes related to the Brewer–Dobson
circulation.
We also estimated regional and vertically resolved ozone trends in the polar
regions. As far as we know, this is the first such analysis using limb
satellite measurements. We found statistically significant positive trends
in the NH polar middle stratosphere (25–30 km). In the SH polar regions, the
estimated ozone trends are mostly positive, but they are not statistically
significant.
The MEGRIDOP dataset can be used in different analyses. In particular, it
can be used for intercomparison of climate data records from
ground-based and other satellite measurements and chemistry-transport models
in the future.
Data availability
The
dataset is available through open access at https://climate.esa.int/en/projects/ozone/data/ and at ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office, last access: 25 April 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-6707-2021-supplement.
Author contributions
VFS designed the study, performed analyses and wrote the major part of the manuscript. MS participated in trend analyses. MS, JT, EK, DD, CR, DZ, AR, CA, JPB, MW, AL, GPS, TvC, LF, NL, MvR and CR provided the data and contributed to analyses and manuscript writing.
Competing interests
Gabriele P. Stiller, Thomas von Clarmann and Michel van Roozendael are editors of ACP, but have not been involved in the evaluation of this paper.
Special issue statement
This article is part of the special issue “New developments in atmospheric limb measurements: instruments, methods, and science applications (AMT/ACP inter-journal SI)”.
Acknowledgements
The work is performed in the framework of the ESA Ozone_cci+
project. The GOMOS ALGOM2s dataset was created in the framework of ESA ALGOM
project. The KIT team would like to thank the European Space Agency (ESA)
for giving access to MIPAS level-1 data. The SCIAMACHY ozone retrieval was
funded in parts by the ESA, the German Academic Exchange Service (DAAD), the
German Aerospace Agency (DLR), and the University and State of Bremen. The
dataset was calculated with resources provided by the North-German
Supercomputing Alliance (HLRN). The study is also a contribution to the German Ministry of Education and Research (BMBF) Synopsis Project. The FMI team thanks the Academy of Finland. The authors thank the Canadian Space Agency. Work at the Jet
Propulsion Laboratory, California Institute of Technology, was performed
under contract with the National Aeronautics and Space Administration
(NASA).
Financial support
This research has been supported by the European Space Agency (Ozone_cci+ project, contract 4000126562/19/I-NB), the EU Copernicus Climate Change Service for Atmospheric Composition ECVs (contract C3S_312b_Lot2_DLR_2018SC1), the German Academic Exchange Service (DAAD), the German Aerospace Agency (DLR), the University and State of Bremen, the German Federal Ministry for Economic Affairs and Energy (project SEREMISA), the Academy of Finland (the Centre of Excellence of Inverse Modelling and Imaging (decision 336798)), the Canadian Space Agency and the National Aeronautics and Space Administration (Contract 80NM0018D0004).
Review statement
This paper was edited by Jens-Uwe Grooß and reviewed by two anonymous referees.
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