Ozone soundings from nine Nordic stations have been homogenized and interpolated to standard pressure levels. The different stations have very different data coverage; the longest period with data is from the end of the 1980s to 2014.

At each pressure level the homogenized ozone time series have been analysed with a model that includes both low-frequency variability in the form of a polynomial, an annual cycle with harmonics, the possibility for low-frequency variability in the annual amplitude and phasing, and either white noise or noise given by a first-order autoregressive process. The fitting of the parameters is performed with a Bayesian approach not only giving the mean values but also confidence intervals.

The results show that all stations agree on a well-defined annual cycle in the free troposphere with a relatively confined maximum in the early summer. Regarding the low-frequency variability, it is found that Scoresbysund, Ny Ålesund, Sodankylä, Eureka, and Ørland show similar, significant signals with a maximum near 2005 followed by a decrease. This change is characteristic for all pressure levels in the free troposphere. A significant change in the annual cycle was found for Ny Ålesund, Scoresbysund, and Sodankylä. The changes at these stations are in agreement with the interpretation that the early summer maximum is appearing earlier in the year.

The results are shown to be robust to the different settings of the model parameters such as the order of the polynomial, number of harmonics in the annual cycle, and the type of noise.

Tropospheric ozone is a short-lived trace gas with a lifetime of 3–4 weeks on
average and a following strong temporal and spatial variability.
Tropospheric ozone is dangerous to human health and crops. Furthermore,
tropospheric ozone is a greenhouse gas – and therefore often
characterized as a short-lived climate forcer or short-lived climate
component – and the increase over the 20th century has led to a
considerable positive (warming) radiative forcing only exceeded by
that contributed by carbon dioxide and methane

Tropospheric ozone originates from intrusions of stratospheric air or is
produced in the troposphere itself by photo-chemical processes involving
precursors such as nitrogen oxides. The precursors may be of natural origin or
due to anthropogenic activities

In the 20th century, globally there has been a general increase in
tropospheric ozone in qualitative agreement with the increasing
levels of nitrogen oxides from pollution. In the last part of the
20th century ozone level stabilized over Europe and North America

In the Northern Hemisphere (NH), tropospheric ozone peaks in the
late spring or summer

There has been evidence found that the seasonal cycle of tropospheric
ozone in the NH mid-latitudes has changed so that the peak now appears
earlier than 20 years ago

In the Arctic balloon soundings are relatively scarce and the measurement
periods vary from station to station. The longest data series are
from Resolute, Canada

Here, we investigate ozone variability over nine northern high-latitude stations, with an emphasis on the measurements made over northern Europe and Greenland. We focus on the low-frequency variability and on the changes in the annual cycle for which previous results in the Arctic are scarce. The present study includes recent ozonesonde measurements obtained in the period from the early 2000s to 2014, which have not been analysed in details before. This results in a 27-year data set for the longest record. We include ozonesonde data from Bear Island, Ørland, and Gardermoen that have not been considered in the previous studies of tropospheric ozone. The measurements are homogenized according to current recommendations. The ozone time series from the individual stations are analysed with a model, which includes both low-frequency variability and the annual cycle with higher harmonics. The potential for low-frequency variability is implemented both as a general polynomial trend and time-varying annual amplitudes and phases. The noise is either white or given by a first-order autoregressive process. The model is non-linear and may include a large number of parameters. The fitting of these parameters is performed with a Bayesian approach. The Bayesian approach gives us mean values and uncertainties not only of the parameters but also on derived quantities such as temporal differences and annual cycles. This approach naturally handles strongly irregular sampled time series including extended periods without data and is therefore favourable for the analysis of ozone time series.

The ozonesonde is an electrochemical device containing two electrode
chambers: an anode chamber filled with potassium iodide saturated
phosphate buffer and a cathode chamber filled with same phosphate
buffer containing a well-defined concentration of potassium iodide

Different types of ozonesondes have been in use over the years, the
primary two types being manufactured by EnSci and Science Pump. Both
types are constructed as described above. For each ozonesonde type
there is a recommended composition of the anode and cathode solutions in
use. Problems arise with a change to a different brand of ozonesonde. Such
changes have taken place at all stations with the EnSci type becoming
increasingly popular (see Fig.

The stations included in the study. Results from Resolute and Alert are shown in the Supplement.

The geographic distribution of the included stations are shown
in Fig.

Geographical positions of the ozonesonde stations: Eureka (Eu), Ny Ålesund (Ny), Thule (Th), Bear Island (BI), Scoresbysund (Sco), Sodankylä (So), Ørland (Or), Gardermoen (Ga), Lerwick (Le). Also, Resolute (Re) and Alert (Al) are shown.

Timing of soundings. Each dot represents a sounding reaching at least 250 hPa. Red dots indicate EnSci type sondes and black dots Science Pump sondes. Blue dots indicate that the type is not reported in the records.

Ozone partial pressure (mPa) as a function of time and pressure for the nine stations.

For each station and for each homogenized ozone sounding, the ozone has
been interpolated to standard pressure levels between 900 and 10 hPa
(900, 800, … 300, 250, … 100, 80, 70 … 10 hPa.). The
resulting ozone fields are shown as a function of time and pressure in
Fig.

Ozone at 500 hPa (partial pressure mPa) for the nine stations.
Observations (black), model mean fit (cyan), and polynomial part of the model
(green) as a function of time at 500 hPa. Model settings:

The polynomial part of the model as a function of time at 500 hPa.
Green curve shows posterior mean, black curves indicate the 95 %
confidence intervals for each point in time. Model settings:

At each pressure level we want to model the temporal development of
ozone. We are particularly interested in potential low-frequency trends,
the annual cycle, and changes in the annual cycle. We therefore use a
model that contains a trend, an annual cycle, and noise. The model has
the form

The model has the following properties.

The trend consists of a constant

The annual cycle consist of a sum of

The noise is either independent Gaussian with variance

Then, the model totally includes

The model is non-linear and includes a considerable number of
parameters. The data (Fig.

We therefore choose a Bayesian approach for interference

This approach not only produces ensembles of all parameters but also of all derived quantities such as trends, annual cycles, and changes in the annual cycles. These ensembles give the posterior distributions of the quantities under consideration and from these distributions we calculate and report the posterior mean and the 95 % confidence intervals (or credible intervals as they are called in the Bayesian literature). Thus, this approach can provide mean and confidence intervals for, i.e. the difference of the annual cycle between two periods. We produce a large ensemble (20 000 members) of the posteriors and make sure that the process has converged. We discard the first half of the ensemble to avoid transients.

Given the large differences in data coverage among the different stations,
we can not expect that all station can provide sufficient information to
constrain models with a high number of parameters. We therefore begin the
analysis with a simple version of the model including only the polynomial
trend and a fixed annual cycle. In Sect.

Figure

It is obvious that the Bayesian procedure has produced reasonable fits dominated by an annual cycle and including a weak inter-decadal variability. It is also obvious that there is a considerable residual scatter at all stations. This scatter is the expression of dynamical and chemical processes in the atmosphere as well as measurement noise. Residuals calculated as the difference between the mean model and the original data are shown in Fig. S2 for Ny Ålesund at 500 hPa. In the upper panel the residuals are shown as a function of time, the middle panel shows the residuals as a function of the day of the year, and the lower panel shows the histogram of the residuals. In general the residuals are stationary with little low-frequency structure. The distribution is approximately symmetric and not far from a Gaussian but with some outliers. There is no or only a weak seasonal cycle in the residuals. These results are characteristic for levels below 300 hPa at all stations. Above 300 hPa an annual cycle is seen in the residuals with the largest deviations in the winter. This is likely related to the strong stratospheric variability in this season. In particular at 300 hPa the residuals are positively skewed, probably because this level moves in and out of the stratosphere. In the stratosphere the residuals are again almost Gaussian distributed.

Figure

While the discussions above dealt with the 500 hPa layer we now consider
all layers in the troposphere. Figure

The long-term mean as a function of pressure (solid curves). Dashed
curves indicate the 95 % confidence intervals. Model settings:

The polynomial part of the model as a function of time and pressure.
The temporal means are shown in the panel to the right as a function of height.
The contours show the anomalies with respect to this mean. Shaded regions are
where the anomalies are statistically different from the temporal means at 99
and 95 % levels. Model settings:

The contour plots in Fig.

This is in general agreement with the discussion above about the variability at 500 hPa. The signal is weak or absent at the tropopause level but note also that a strong signal of the same sign as in the troposphere is found in the lower stratosphere. This might indicate that the low-frequency variability in the troposphere is linked to that of the stratosphere through dynamical processes.

The annual cycle as a function of day of year at 500 hPa. The full curve
shows the posterior mean; dashed curves indicate the 95 % confidence
intervals for each day of the year. Model settings:

For each station Fig.

Mean annual cycle as a function of pressure level. Model settings:

The mean annual cycle as a function of height is shown in
Fig.

At the near surface at 900 hPa there is some evidence for a qualitatively different annual cycle with a secondary maximum in autumn. This is observed for the most northern and eastern stations: Ny Ålesund, Thule, and Eureka. This is also found in the two additional Canadian stations, Alert and Resolute (Figs. S2 and S3).

Average annual cycles over 1995–2000 (cyan) and 2007–2012 (red) at
500 hPa. Full curve is the posterior mean, dashed curves indicate the
95 % confidence intervals. Model settings:

Difference between average annual cycles over 2007–2012 and
1995–2000 (i.e. average over 1995–2000 subtracted from average over
2007–2012) as a function of pressure level. Shaded regions are where the
anomalies are statistically different from the temporal means at 99 and
95 % levels. Model settings:

As the sondes also record temperatures and heights, we can calculate the
tropopause pressure for each sounding according to a lapse-rate criterion.
Here we define the tropopause as the lowest height between 450 and 85 hPa,
where the lapse rate drops below 2

Thus, one could speculate that at the lowest levels the annual cycle represents a combination of in situ processes and transport, while it in the upper parts of the troposphere (above 400 hPa) is related to the transport or dynamical effects from the stratosphere.

Top: the polynomial part of the model as a function of time and
pressure for Scoresbysund. The models are (left)

We saw in the last section that the annual cycle was well modelled
and almost identical for all stations. This provides some hope for
that we have enough information to detect potential changes in the
annual cycle. We limit the following analysis to the four stations
with best data coverage: Scoresbysund, Sodankylä, Ny Ålesund,
and Eureka. We now extent the model from the last section by setting

The results at 500 hPa are shown in Fig.

The differences between the mean annual cycles over 2007–2012
and 1995–2000 are shown as a function of pressure level in
Fig.

While the significance of the changes at Eureka are weak, the pattern of
these changes agrees with the significant patterns found at Alert and
Resolute (Figs. S2 and S3). For these stations the ozone levels in summer have
increased and the autumn levels have decreased. As for the low-frequency
variability (Sect.

Our model allows for many different settings of the parameters and it is not obvious which setting that is the optimal choice. We have, for example, in the previous discussion restricted ourselves to model setups with white noise.

In this section we briefly discuss the robustness of the results to changes in the parameters of the model. We will restrict the presentation to Scoresbysund for the low-frequency variability and to Ny Ålesund for the changes in annual cycle, but similar results are found at other stations.

The upper panels in Fig.

The lower panels in Fig.

These results are typical for the stations with best data coverage. Some sensitivity is seen for stations with large gaps between soundings. It should also be noted that at the levels from 300 hPa and above the residuals are strong and are positively skewed. This behaviour is probably due to the proximity to the stratosphere and the positive excursions related either to variation of the tropopause height or to intrusions of ozone-rich stratospheric air into the troposphere.

We have analysed ozone long-term sounding records from nine Nordic stations. The different stations have very different data coverage. The longest period with data is from the end of the 1980s to 2014. The ozonesonde data were homogenized according to the recent, recommended transfer functions. We interpolated the homogenized series to standard pressure levels and in the following analysis we focused on the tropospheric levels. We applied a model that includes both a low-frequency variability in form of a polynomial, an annual cycle with harmonics, the possibility for low-frequency variability in seasonal amplitude and phasing, and noise that could be either white or a first-order autoregressive process. The fitting of the parameters were performed with a Bayesian approach giving not only the posterior mean values but also 95 % confidence intervals. This approach is appropriate for strongly scattered data such as the ozone soundings. It can deal with data gaps and makes use of all the information in the data in contrast to methods based on producing monthly averages.

Our main findings are the following.

The long-term averages have the same profile for all stations. The mixing ratios decrease with height from the largest values of 3–4 mPa at the lowest layer to a well-defined minimum around 400 hPa.

All stations agree on a well-defined annual cycle in the free troposphere with a relatively confined maximum in the early summer. While the amplitude of the annual cycle does not vary much with height in the troposphere the spring/summer maximum appears somewhat (about 50 days) earlier in the lowest layers compared to the middle troposphere.

Regarding the low-frequency variability, we find that Scoresbysund, Ny Ålesund, Sodankylä, Eureka, and Ørland show a consistent and significant structure with a maximum near 2005 followed by a decrease. This signal has the same sign for all heights and an amplitude that decreases with height. There is some evidence for a different regional signal at the Canadian stations with ozone levels increasing after 2005.

Some changes in the annual cycle were found for Ny Ålesund, Scoresbysund, and Sodankylä with the most significant changes found for Ny Ålesund. The changes are consistent between the three stations – although there are differences in the vertical profile of the changes – and are in agreement with the notion of the summer maximum appearing earlier in the year.

The results were shown to be robust to the different settings of the model parameters such as the order of the polynomial, number of harmonics in the annual cycle, and type of noise.

The significant maximum at Scoresbysund, Ny Ålesund, Sodankylä,
Eureka, and Ørland around 2005 and the following decrease have not
been reported before regarding observations in the free troposphere and
the Arctic. Previous work

Our finding that ozone peaks in spring/summer is in agreement with what
is found for the NH

The decrease in Arctic tropospheric ozone since 2005 may be explained
by the corresponding decrease in nitrogen oxide level observed in
mid-latitude Europe, where current levels now are down to 50 % of 1990
level

The ozone soundings can be downloaded from the World Ozone
and UV database at Toronto (

The authors declare that they have no conflict of interest.

This article is part of the special issue “Twenty-five years of operations of the Network for the Detection of Atmospheric Composition Change (NDACC) (AMT/ACP/ESSD inter-journal SI)”. It does not belong to a conference.

We thank David Tarasick (Eureka), Peter von der Gathen (Ny Ålesund), and Dave Moore (Lerwick) for the ozone sounding data. This study was supported by the NMR KOL group (project no. NMR KOL 1402). Research at FMI was also supported by an EU Project GAIA-CLIM, the ESA's Climate Change Initiative programme and the Ozone_cci subproject in particular. Edited by: Hal Maring Reviewed by: two anonymous referees