ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-9347-2017Trends and annual cycles in soundings of Arctic tropospheric ozoneChristiansenBoboc@dmi.dkhttps://orcid.org/0000-0003-2792-4724JepsenNisKiviRigelhttps://orcid.org/0000-0001-8828-2759HansenGeorgLarsenNielsKorsholmUlrik SmithDanish Meteorological Institute, Research and Development, Copenhagen, DenmarkFinnish Meteorological Institute, Arctic Research, Sodankylä, FinlandNorwegian Institute for Air Research, Fram Centre, Tromsø, NorwayBo Christiansen (boc@dmi.dk)4August20171715934793647April201718April201730June20176July2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/9347/2017/acp-17-9347-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/9347/2017/acp-17-9347-2017.pdf
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.
Introduction
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 profiles from satellites have only been available for
a decade; therefore, information about long-term trends and variability mainly
comes from in situ measurements such as balloon soundings.
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 see the review by.
The sinks are photo-chemical processes and dry deposition at the surface.
While the photo-chemical processes dominate globally, model studies
indicate that in the Arctic anthropogenic pollution
from the Northern Hemisphere is the dominant source of ozone from the
surface to 400 hPa and that the stratospheric influence is the main
contribution at pressures less 400 hPa. The anthropogenic sources
may either be formed in situ or transported to the site of reaction.
In particular, summer emissions from fires in Russia and North
America impact the tropospheric ozone in the Arctic. Nitrogen oxides are
considered especially important in this respect, and apart from originating
from anthropogenic activities they may also be formed in lightning
processes . The influx from the stratosphere may be
caused by tropopause foldings as has been demonstrated using backwards
trajectory calculations . Synoptic-scale processes as
represented by the 250 hPa geopotential height have also been successfully
linked to the recent ozone increases in the lowermost stratosphere
. Analysis of observations in the 2008 International
Polar Year indicates that stratosphere–troposphere
exchange is larger over Greenland than over Canada.
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
; see also the reviews of and
. A flattening of the trend is
also seen in other regions over the last 10–15 years – although with
many regional differences – and it is likely that this is at least
partly due to the fact that the emission of precursors has been curbed
. It should be noted that changes in tropospheric
circulation patterns also may play a role .
In the Northern Hemisphere (NH), tropospheric ozone peaks in the
late spring or summer e.g.. The
spring–summer peak is often attributed to enhanced photo-chemical
production and the latest occurring of the peak is
often found in the most polluted continental regions. However, it has also
been argued that the stratosphere–troposphere exchange may play a role.
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 . finds
in a study of five stations that the change in the peak occurrence is 3–6 days
per decade since 1970. extended the analysis
including additional sites and confirmed that there is a general shift
although not observed at all sites. Possible reasons for the changes in
the seasonal cycle are changes in atmospheric patterns and emissions.
also called for additional analysis including e.g. the
polar regions.
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 . In the European sector of
the Arctic and over Greenland ozonesondes have been flown since
late 1980s . Accordingly, the reported long-term
changes in tropospheric ozone are scattered. found
decreasing tropospheric ozone at Resolute, Canada, in the period 1970–1996. Also, and have reported ozone
decreases at Resolute. Negative trends in tropospheric ozone over Canada
in the period 1980–1993 were also reported by
and . Later, also noted that
when the period 1991–2001 is considered the trends are positive.
found for three stations in Arctic Canada that
negative trends in the beginning of the period 1980–2010 had been
neutralized by positive trends later in the period.
studied the variations in ozone profiles using ozonesonde
observations from seven northern high-latitude stations from 1989 to
2003. In the free troposphere they found a statistically significant
increase of 11 % in this period with largest values in January to April,
the period of greatest inter-annual variability. They attributed the
observed change to the combined increase in the stratosphere–troposphere
exchange and the transport of precursors towards the higher latitudes.
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 data and methodOzonesonde data
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
. During ascent through the atmosphere a
constant volume pump is drawing atmospheric air through the cathode
chamber. The content of ozone in an air sample is reacting with the
potassium iodide and gives rise to a current proportional to the ozone
amount. The electrode chambers and the pump is installed in a Styrofoam
box for insolation purposes. To keep the buffer liquids from freezing
during ascent, a simple heater element is keeping the temperature in box
at 10–25 ∘C. A thermistor is sensing the actual temperature inside
the box. On the outside of the Styrofoam box a regular radiosonde is
mounted. The radiosonde is measuring pressure, temperature, humidity, wind
speed, and wind direction during ascent. The ozone current and the
box temperature is via an interface transmitted to a ground receiver
along with the radiosonde parameters. The ozonesonde and the radiosonde
are lifted with a helium or hydrogen filled meteorological balloon. At
best the balloon may reach an altitude at 35–40 km. The typical vertical
resolution is around 10 m using 2 s intervals for sampling. However,
the effective vertical resolution is of the order of 100–150 m, given
that the response time of the ozone sensor is 20–30 s. Uncertainty of
the ozone measurements by electrochemical sondes in the stratosphere
is about 5 % .
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. ). Historically
many launches have been made using a sensing solution recommended for
Science Pump ozonesondes in case of switching to the use of EnSci
type ozonesondes. To investigate the difference between the two sonde
types and sensing solutions, a number of in situ measurements have
been performed in the laboratory and in the field
. These measurements have resulted in the
current recommendations for the ozonesonde preparations .
In this work ozonesonde data were homogenized according to the recommended
transfer functions for data homogenization . A typical
example of the conversion is from an EnSci sonde (e.g. of 1.0 % sensing
solution, 10 g L-1) to a Science Pump sonde of the same solution. In this
case the conversion ratio is 0.96 for atmospheric pressures greater
than 30 hPa, while it is 0.764+0.133log10(p) for atmospheric
pressures smaller than 50 hPa. Here, p is the atmospheric pressure in
hPa. A similar formula describes the conversion between different sensing
solutions. The Danish, Norwegian, and Finnish stations were homogenized
by the authors of the present paper, while the data from Lerwick, Ny Ålesund,
and Eureka were homogenized locally (see the Acknowledgements).
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. , and the covered time periods are summarized
in Table . The number of soundings for each station as a
function of year is shown in Fig. . This figure also
shows the type of ozone sonde used. The longest time series span the
period from the late 1980s to 2014. The time series of Bear Island,
Gardermoen, and Ørland are particular brief spanning less than
10 years. In general the soundings are highly irregular timed with
occasional years with very few or none soundings. We also note that
the details vary a lot among the stations. The average yearly number of
soundings are the largest (around 90) for Ny Ålesund and the lowest for Thule
(around 20). There are in general more soundings in winter and spring
than in summer and autumn (Table shows the seasonal average
of number of soundings disregarding years without soundings). This
is due to the frequent ozonesonde campaigns to investigate the
stratospheric vortex ozone depletion during the winter/spring season
. Two additional stations, Resolute
and Alert, with long records have been studied. However, as these stations
are close to Alert and show very similar behaviour, the results from these
stations are shown in Figs. S2 and S3 in the Supplement.
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. for each station. As expected there is a
maximum on the lower stratosphere. Here and in the rest of the paper ozone
partial pressure is measured in millipascal (mPa). Time series of the free
tropospheric ozone at 500 hPa are shown in Fig.
(black dots). Here, we already note that these ozone records show a
background level of 2–4 mPa and that the ozone records have large annual
cycles and a considerable amount of scatter.
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: npol=4,
ncyc=2, ntra=ntrθ=0, and white
noise.
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: npol=4,
ncyc=2, ntra=ntrθ=0 and white
noise.
Model description
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
y=λ0+λ1t+λ2t2+…a1sin(2πt+θ1)+a2sin(2π2t+θ2)…+ξ,
where y is the ozone and t is the time (in years). Note that
the amplitudes, ai, and phases, θi, may depend on time as
detailed below.
The model has the following properties.
The trend consists of a constant λ0, a linear trend λ1t, and higher-order polynomials up to λnpol-1tnpol-1.
The annual cycle consist of a sum of ncyc sinusoidals,
aisin(2πit+θi), with frequencies 1,2,3…ncyc.
The higher harmonics allow the seasonal cycle to be
asymmetric. The amplitudes and phases of the cycles have trends
with ntra and ntrθ terms: ai=ai,0+ai,1t+…ai,ntratntra, θi=θi,0+θi,1t+…θi,ntrθtntrθ. This allows the annual cycle to change over time.
The noise is either independent Gaussian with variance σ2
or an first-order autoregressive process (AR1) with coefficient θ
and variance σ2.
Then, the model totally includes 2+npol+ncyc(1+ntra+ntrθ) parameters under AR1 noise and one less
under Gaussian noise.
The model is non-linear and includes a considerable number of
parameters. The data (Fig. ) are irregular samples with
strong changes in the number of soundings over time but also with a strong
seasonal cycle in the number of soundings. Calculating monthly or annual
means followed by an estimation of the annual cycle and trends from these
means – as done in some previous studies – is sub-optimal. It will,
in particular, make the uncertainty difficult to estimate trustfully.
We therefore choose a Bayesian approach for interference
see e.g.. The Bayesian approach does not
require regular temporally gridded data but can work directly
with the original sampling. Bayesian approaches are becoming
more frequent in many different areas of atmospheric and climate
sciences see e.g.. Probably
the biggest difference between Bayesian and sequential methods is that in
the Bayesian approach the parameters of the model can be seen as random
variables and that this approach can systematically include prior
information. More precisely, in the Bayesian approach a posterior distribution
is calculated as the product of the likelihood of the data given the
model and a prior distribution describing our previous knowledge of
the parameters of the model. The posterior distribution includes all
the wanted information, e.g. joint and marginal distributions of all
the model parameter. Unfortunately, this information is not easily
accessible as the posterior is not normalized and of high dimension
(the number of parameters in the model). The posterior is therefore
analysed by numerical methods. Here, this analysis is performed
with a simple Metropolis–Hastings algorithm . The
Metropolis–Hastings algorithm is a Markov chain Monte Carlo method that
obtains samples from the posterior, which can then be used to approximate
the distribution.
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.
Results
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.
this model is used to study the long-term mean and the trends, and in
Sect. it is used to study the mean annual cycle.
In Sect. we extent the model to include trends
in the amplitudes and phases of the annual cycle so that changes in
the annual cycle can be studied. We only apply the extended model to
the four stations with the best data coverage. In all subsections we
begin by considering the 500 hPa level before we proceed to other levels
of the troposphere. As mentioned, the Bayesian approach gives not only
point values but also the whole posterior distributions; therefore, we are able to
produce confidence intervals for all the studied quantities.
Mean and trends
Figure shows for each station at 500 hPa the raw
data (black points), the posterior mean of the non-stochastic part
of the model, i.e. the polynomial part and the annual cycle (cyan),
and the posterior mean of the polynomial part of the model (green)
alone. The model includes a third-order polynomial (npol=4) and
two components in the annual cycle (ncyc=2). The model does not
include trends in the amplitudes and phases of the annual cycle and the
noise is assumed white.
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 shows both the mean polynomial part of
the model (the cyan curve in Fig. ) and its 95 %
confidence interval for each point in time at 500 hPa. For all stations
the long-term background value is around 3 mPa and the polynomial part
is relatively flat with some weak low-frequency variability. The 95 %
confidence intervals are quite large relative to the low-frequency
variability. This mainly reflects the data coverage but the confidence
intervals also increases near the beginning and end of the time series
where data are limited because of the asymmetry. For Scoresbysund,
Sodankylä, Ny Ålesund, and Eureka some significant albeit weak
low-frequency variability can be discerned. At Scoresbysund the ozone
partial pressure increases until a maximum is reached near 2007 followed
by a weak decrease. Ny Ålesund shows similar behaviour but now with
the maximum around 2003. Sodankylä also shows a decrease in recent
years with a maximum around 2005. However, Eureka shows a qualitative
different variability with a strong increase from 1993 to 2000 followed
by a quiet period until 2008 after which it again increases. The same
behaviour is found for the nearby stations, Alert and Resolute (Figs. S2
and S3). At Thule, Bear Island, Gardermoen, and Lerwick no significant
trends are found.
While the discussions above dealt with the 500 hPa layer we now consider
all layers in the troposphere. Figure shows the long-term mean
as a function of height. We see that the form of the vertical variations are
identical for all stations. At the lowest level, 900 hPa, the mean ozone
level is between 3 and 4 mPa for all stations. The ozone content then
decreases with height throughout the troposphere until a well-defined
minimum of approximately 2.5 mPa is reached around 300–400 hPa. The
ozone content then increases quickly with height when the stratosphere is
reached. Note that in the troposphere it is discernible that stations
at lowest latitude have larger ozone mixing ratios.
The long-term mean as a function of pressure (solid curves). Dashed
curves indicate the 95 % confidence intervals. Model settings:
npol=4, ncyc=2, ntra=ntrθ=0, and white noise.
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: npol=4, ncyc=2,
ntr1=ntrθ=0 and white noise.
The contour plots in Fig. show the anomalies at
each level, i.e. the deviations from the long-term mean (the right-hand
plots in each panel show the long-term mean as in Fig. ). Shaded
areas indicates regions where the anomalies are significantly different
from zero, i.e. where the ozone content can be considered different
from the long-term mean. In agreement with the results at 500 hPa, we do
not find much significant long-term variability at Thule, Bear Island,
Gardermoen, and Lerwick. In particular for Bear Island and Gardermoen
this might be connected to the brief span of the observations. At the
other stations – Scoresbysund, Sodankylä, Ny Ålesund, Eureka,
and Ørland – we find a consistent and significant signal throughout
the troposphere. This signal in the troposphere has in general the same
sign at all heights and values that decreases with the height. Except for
Eureka there is a general agreement at these stations that a significant
maximum was reached in the years around 2005 although the exact year of
the maximum varies. At Eureka the ozone content increases after 2005,
a result that is also found for Alert and Resolute (Figs. S2 and S3).
Although the significance of the trends at Thule is weak, these trends also
to some extent resemble those of Eureka pointing towards a distinct
regional behaviour.
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: npol=4,
ncyc=2, ntr1=ntrθ=0 and white
noise.
Mean annual cycle
For each station Fig. shows both the mean
annual cycle as well as the 95 % confidence interval for each day of the year
at 500 hPa. The annual cycle has a strong similarity for all stations. It
has a minimum in winter, a maximum in early summer, and a peak-to-peak
amplitude of approximately 1 mPa. We also note that the annual cycle would
not be well modelled with a single sinusoidal as the early summer peak
is more temporal confined than the winter minimum. The widths of the 95 %
confidence intervals reflect the data coverage and are largest for Thule,
Gardermoen, Ørland, and Bear Island.
Mean annual cycle as a function of pressure level. Model settings:
npol=4, ncyc=2,
ntra=ntrθ=0, and white noise. For Scoresbysund,
Sodankylä, Ny Ålesund, and Eureka the annual cycle of the tropopause is
also shown (full black curve) together with its ±2σ confidence
levels (dashed black curves).
The mean annual cycle as a function of height is shown in
Fig. for each station. The annual cycle is rather
similar for all stations consistent with the results for 500 hPa. For most
stations there is a clear change of the phase of the annual cycle with
height; the spring/summer maximum appears earlier at the lower levels
than in the middle of the troposphere. This phase change is typically a
couple of months. In the lower stratosphere the annual cycle again has
the maximum earlier in the year. The amplitude of the annual cycle is
relatively constant with height.
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: npol=4,
ncyc=3, ntra=ntrθ=2, and white
noise.
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: npol=4, ncyc=3,
ntra=ntrθ=2, and white noise.
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 ∘C km-1.
The annual cycle of
the tropopause (monthly values) is included in Fig.
for the longest records: Scoresbysund, Sodankylä, Ny Ålesund, and
Eureka. The general structure – high tropopause pressure in spring and
low tropopause pressure in autumn – is the same as reported in e.g.
. As expected the tropopause in general coincides with
the levels where the vertical gradient in the ozone is largest.
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) npol=4,
ncyc=3, and ntra=ntrθ=2, and white
noise; (right) npol=5, ncyc=3,
and ntra=ntrθ=3, and AR1 noise. Compare also to the top
right plot in Fig. , which does not include trends in
annual cycle (npol=4, ncyc=2, ntr1=ntrθ=0 and white noise). Bottom: difference between average
annual cycles over 1995–2000 and 2007–2012 as a function of pressure level
for Ny Ålesund. Left: npol=4, ncyc=1,
ntra=ntrθ=1, and AR1 noise. Right:
npol=5, ncyc=3,
ntra=ntrθ=3, and AR1 noise. Compare also to
the lower left panel in Fig. .
Changes in the annual cycle
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
ntra=ntrθ=2 and thereby allowing both the amplitudes
and the phases of the annual cycle to vary in time like a second-order
polynomial.
The results at 500 hPa are shown in Fig. , where
the annual cycles averaged over 1995–2000 and 2007–2012 are shown
together with their 95 % confidence intervals for each day of the year.
It should be noted that there are large uncertainties connected to the
changes in the annual cycles. The only significant change is found at
Ny Ålesund, which shows a slight, significant decrease from 0.9 to 0.8 in
the peak-to-peak amplitude. There also seems to be a slight change in the
phase with the maximum appearing a little (20 days) earlier in the later
period. For the other stations there is very little and insignificant
change in the amplitude and phase of the annual cycle at 500 hPa.
The differences between the mean annual cycles over 2007–2012
and 1995–2000 are shown as a function of pressure level in
Fig. . The significant change found at Ny Ålesund
at 500 hPa seems consistent with other levels in the troposphere
for this station. Some significant changes are now also found for
Scoresbysund and Sodankylä. These changes consist of an amplification
of the increasing spring branch of the annual cycle and weakening of the
summer maximum. Thus, the changes in the annual cycles at Ny Ålesund,
Scoresbysund, and Sodankylä have the same sign and patterns. Together
this is consistent with the notion of the summer maximum appearing
earlier in the year.
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. ), this might point towards a
distinct regional behaviour.
Robustness of the results
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. show the polynomial part of
the model for Scoresbysund as a function of height for model settings with
either white noise or AR1 noise. The model settings also include trends
in the annual cycle, which was not the case in Fig. .
We observe that all three model settings agree on the shape of the
low-frequency variability and, in particular, that they agree on the maximum
obtained around the year 2005.
The lower panels in Fig. show the difference in mean
annual cycles over 2007–2012 and 1995–2000 for Ny Ålesund for two
different settings, which include a different number of seasonal harmonics
(also compare the bottom right panel in Fig. ). We
observe that all model settings agree on the pattern of the change in
the annual cycle in the troposphere. Regarding the amplitude there are
some smaller differences with the simplest model (fewest parameters)
having the largest changes.
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.
Conclusions
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 covering data from 1989 to 2003
suggests a linear increase in the free troposphere of about 11 %
consistent with our observations for Thule, Scoresbysund, Ny Ålesund,
Eureka, Sodankylä, and Ørland. Scoresbysund, Eureka, Ny Ålesund,
and Sodankylä were also included in the study by .
The observed change was suggested to be due to changes in the Arctic Oscillation.
Also, found positive trends for Canadian
stations in the period 1991–2001 in contrast to the negative trends found
when the longer period 1980–2001 is considered.
did not find any overall trends in tropospheric ozone for three stations in
the Canadian Arctic in the period 1980–2010; declines in the beginning
of the period have rebounded. Here, we did not see any negative trends
before the year 2001, except perhaps for the brief series at Bear Island.
Our finding that ozone peaks in spring/summer is in agreement with what
is found for the NH . The change in the
annual cycle so that the peak now appears earlier in the year has not been
reported before for the Arctic but is in agreement with what is found
for mid-latitudes , although significant
changes are not found for all stations.
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 . Nitrogen oxide is an important precursor for the
production of tropospheric ozone, but this will still require transport
of this species from Europe to the Arctic. Therefore, the change in free
tropospheric ozone in the Arctic may reflect changes in both precursors
and in transport, while possible changes in the stratosphere–troposphere
exchange should be also considered.
The ozone soundings can be downloaded from the World Ozone
and UV database at Toronto (10.14287/10000001, WMO/GAW Ozone Monitoring Community, 2017) and
from the NDACC database (http://www.ndsc.ncep.noaa.gov/data/, NDACC,
2017).
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-9347-2017-supplement.
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.
Acknowledgements
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
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