ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-13869-2017Reanalysis of and attribution to near-surface ozone concentrations in Sweden
during 1990–2013AnderssonCamillacamilla.andersson@smhi.seAlpfjordHeléneRobertsonLennartKarlssonPer ErikEngardtMagnuzhttps://orcid.org/0000-0002-4695-1106Swedish Meteorological and Hydrological Institute,
60176 Norrköping, SwedenSwedish Environmental Research Institute, P.O. Box 53021,
40014 Gothenburg, SwedenCamilla Andersson (camilla.andersson@smhi.se)22November20171722138691389011April201720June201721September201714October2017This 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/17/13869/2017/acp-17-13869-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/13869/2017/acp-17-13869-2017.pdf
We have constructed two data sets of hourly resolution reanalyzed
near-surface ozone (O3) concentrations for the period 1990–2013 for
Sweden. Long-term simulations from a chemistry-transport model (CTM) covering
Europe were combined with hourly ozone concentration observations at Swedish
and Norwegian background measurement sites using retrospective variational
data analysis. The reanalysis data sets show improved performance over the
original CTM when compared to independent observations.
In one of the reanalyses, we included all available hourly near-surface
O3 observations, whilst in the other we carefully selected
time-consistent observations. Based on the second reanalysis we investigated
statistical aspects of the distribution of the near-surface O3 concentrations, focusing on the linear trend over the 24-year period. We show
that high near-surface O3 concentrations are decreasing and low O3
concentrations are increasing, which is reflected in observed improvement of
many health and vegetation indices (apart from those with a low threshold).
Using the CTM we also conducted sensitivity simulations to quantify the
causes of the observed change, focusing on three factors: change in
hemispheric background concentrations, meteorology and anthropogenic
emissions. The rising low concentrations of near-surface O3 in Sweden
are caused by a combination of all three factors, whilst the decrease in the
highest O3 concentrations is caused by European O3 precursor
emissions reductions.
While studying the impact of anthropogenic emissions changes, we identified
systematic differences in the modeled trend compared to observations that
must be caused by incorrect trends in the utilized emissions inventory or by
too high sensitivity of our model to emissions changes.
Introduction
Elevated concentrations of near-surface ozone (O3) are a major policy
concern given their ability to damage both vegetation (e.g., Royal Society,
2008) and human health (e.g., WHO, 2006). It is also an important greenhouse
gas (e.g., Stocker et al., 2013). Elevated O3 concentrations are formed
in the troposphere by the oxidation of volatile organic compounds (VOCs) and
carbon monoxide (CO), driven by solar radiation in a polluted air mixture
that includes nitrogen oxides (NOx, sum of nitric oxide and nitrogen
dioxide: NO + NO2). Close to combustion sources, the background
O3 concentrations are reduced
through reactions with directly emitted NO (see for example Finlayson-Pitts
and Pitts, 2000). However, further away from the source and with sufficient
availability of VOCs and under favorable weather conditions these NOx
emissions can lead to rises in the O3 concentration. O3 can be
transported to regions far away from the area where it was formed and even
across continents (e.g., Akimoto, 2003; Derwent et al., 2015). Oxidized
nitrogen can also be transported to remote regions as reservoir species, such
as peroxy-acetyl nitrates (PANs). These can be a significant source of
NOx, and alongside naturally emitted biogenic VOCs, cause O3
formation in otherwise non-polluted areas (e.g., Jacob et al., 1993; Fiore et
al., 2011).
European and North American anthropogenic emissions of NOx increased
over most of the 20th century, but have decreased strongly since the 1980s
due to emission controls (e.g., Lamarque et al., 2010; Granier et al., 2011).
Asian emissions have continued to rise under the same period (Ohara et al.,
2007). Jonson et al. (2006) showed that the trend in O3 concentrations in
Europe cannot be fully explained by changes in European precursor emissions.
Through intercontinental transport the increasing precursor emissions in Asia
could contribute to increasing background levels with at least a strong
impact in North America (Vestraeten et al., 2015), whilst the trend in
European background O3 seasonal variation could also be affected by the
decreases in North American precursor emissions (Fiore et al., 2009; Derwent
et al., 2015). Climate also changes over time, causing both changes to the
O3-forming potential: biogenic emissions of O3 precursors and
deposition processes (Andersson and Engardt, 2010). Variability in climate,
such as the North Atlantic Oscillation (NAO), contributes to the variation in
O3 concentrations in the upper troposphere through variations both in the
stratospheric contribution and in the transport patterns (Gaudel et al.,
2015). Although the stratospheric contribution to the O3 concentrations
at the surface is generally small (3–5 ppb(v)) in Europe (Lelieveld and
Dentener, 2000), it can be a relevant contribution to near-surface O3 in
certain areas and time periods (Zanis et al., 2014) and could affect the
observed trend in near-surface O3 (e.g., Fusco and Logan, 2003). Despite
the large number of studies of tropospheric O3, a number of challenges
still remain, such as explaining the near-surface concentration trends (Monks
et al., 2015).
Observations in the northern mid-latitudes, taken either at the surface (Oltmans et
al., 2006) or from ozonesondes and commercial aircraft (Logan et al., 2012),
present the picture of increasing tropospheric O3 concentrations during
the second half of the 20th century (Parrish et al., 2012; Cooper et
al., 2014). The strong increase in near-surface O3 concentrations until
the late 1990s at Mace Head has leveled out to relatively stationary annual
values throughout the 2000s (Derwent et al., 2013; Cooper et al., 2014). At
Pico Mountain Observatory in the Azores, a decreasing O3 concentration
trend was observed during 2001–2011 which was believed to be mainly caused
by decreasing precursor emissions in North America (Kumar et al., 2013). Air
masses with European origin observed at Mace Head show a decrease in
summertime peak O3 concentrations and increase in wintertime, which is
believed to be connected to European NOx policy (Derwent et al., 2013).
O3 concentrations observed at European alpine sites and in ozonesonde
data (MOZAIC) above European cities have decreased since 1998 with the
strongest decrease in summer (Logan et al., 2012).
Several modeling efforts have been conducted to describe the past
near-surface O3 concentration development (e.g., Fusco and Logan, 2003;
Schultz et al., 2007; Pozolli et al., 2011; Xing et al., 2015). Parrish et
al. (2014) present past trends in tropospheric O3 concentrations
modeled with three chemistry–climate models and conclude that while there is
considerable qualitative agreement between the measurements and the models,
there are also substantial and consistent quantitative disagreements. The
models capture only 50 % of the change observed during the last 5–6 decades and little of the observed seasonal differences, and the rate of the
trends are poorly captured. There are ways forward to improve the description
of the trends in models: (1) understanding the processes and improving the
model description of the physics and chemistry for processes of greatest
importance in these models, (2) improving the input data quality and
(3) incorporating observations in the model by data fusion methods to
accurately represent the past statistics in a reanalysis. The first two are
important for conducting scenario calculations, whilst the last is an option
for producing mappings.
If correctly conducted, data fusion will improve the modeled estimates. If
temporal and spatial consistency is not considered, it may however introduce
artificial trends. Artificial trends can for example arise from the
introduction of new observation sites, which reduce the model bias in the
area surrounding the measurement site during the time it is included but not
before. Data assimilation, a subset to data fusion (Zhang et al., 2012), is
the process by which observations of the real world are incorporated into the
model state of a numerical model, in this case into the chemistry-transport
model (CTM; Kalnay, 2003; Denby and Spangl, 2010). Advanced data
assimilation schemes like the four-dimensional variational technique (4DVAR;
e.g., Courtier et al., 1994; Inness et al., 2013) utilize information provided
by satellites and propagate this in space and time from a limited number to a
wide range of chemical components to provide fields that are physically and
chemically consistent with the observations. Inness et al. (2013) performed a
reanalysis of global chemical composition, including O3 concentration,
for 2003–2010 using advanced data assimilation of satellite observations
within the framework of the Monitoring Atmospheric Composition and Climate
(MACC) project. They demonstrated improved O3 and CO concentration
profiles for the free troposphere, but biases remained for the lower
troposphere. Another reanalysis of near-surface O3 concentrations in
Europe, also within the MACC project, was conducted for the period 2003–2012
(Katragkou et al., 2015). In this reanalysis 4DVAR data assimilation was also
used to incorporate retrievals from satellites. The data assimilation reduced
the bias in near-surface O3 concentrations in most of Europe, and it
reproduced the summertime maximum in most parts of Europe, but not the early
spring peak in northern Europe. A third global reanalysis using data
assimilation of satellite data for 2005–2012 showed improved performance
for many chemical species (Miyazaki et al., 2015); however, for the O3 concentrations at the surface, errors remain associated with low retrieval
sensitivity in the lower troposphere and gaps in spatial representation
between the model and observations. In order to improve surface
characteristics, in situ observations of O3 need to be included in the
data assimilation.
When restricting the observations to in situ measurements in Europe, the
beginning of the time period of the reanalysis can be extended further back
in time utilizing simpler variational data analysis techniques. Variational
data analysis in two dimensions (2DVAR) and the analytical counterpart optimal
interpolation can be used as CPU-efficient diagnostic tools to improve
modeled near-surface O3 retrospectively (e.g., Alpfjord and Andersson,
2015; Robichaud and Ménard, 2014).
The MATCH (Multi-scale Atmospheric Transport and CHemistry) Sweden system
(Alpfjord and Andersson, 2015) includes an operational CTM and methods for
variational data analysis of atmospheric concentrations in air and
precipitation. In this study, the MATCH Sweden system is used to conduct a
reanalysis of the hourly near-surface O3 concentrations for Sweden and
Norway during the 24-year period 1990–2013 using 2DVAR. We use
time-consistent input data to avoid the introduction of artificial trends in
the results. In an attempt to understand the trends, we perform model
sensitivity analyses and apply the CTM without variational data analysis.
This approach brings new knowledge to explain the trends in O3
concentrations found in Sweden.
The aims of this study are as follows:
to create a state-of-the art, long-term, temporally and spatially consistent
reanalysis of hourly near-surface O3 concentrations covering the
geographical areas of Sweden and Norway (see Sect. 2);
to evaluate the performance of the O3 reanalysis of the MATCH Sweden
system used in the annual assessment of air quality in Sweden (see
Sect. 3.1);
to investigate trends and extreme values in near-surface O3 in Sweden
(see Sect. 3.2) and its implications on health and
vegetation (see Sect. 3.4);
to understand the causes of the change over time, focusing on contributions
of emission change, lateral and upper boundary concentrations and
meteorological variability (see Sect. 3.3).
Method
In this study we utilize variational data analysis in order to combine the
best qualities of a CTM and long-term measurements to map near-surface
O3 concentrations during a long historical time period (1990–2013). We
focus our study on Sweden, but also include Norway in the variational data
analysis.
For the variational data analysis we use the MATCH Sweden system, which is
briefly explained in Sect. 2.1. Here variational analysis in two dimensions
is applied, and further details are given in Sect. 2.4. Concentration fields
provided by the CTM at each grid point are considered as the “first guess”
(background field/prior information) of our “best estimate” of the state of
the atmosphere before the introduction of observations (Kalnay, 2003). The
method used for the production of the first guess is explained in
Sect. 2.2. The selection of measurements that are included in the variational
data analysis is important, both to avoid artificial trends in the reanalysis
data and in order to select observation sites with corresponding spatial and
temporal representations as in the model. We explain our method for the
selection of measurements in Sect. 2.3.
One aim of this study is to investigate trends in near-surface O3 in
Sweden. To understand the long-term changes in concentration we try to
quantify the causes of change, through model sensitivity analyses, and by
applying the MATCH model without variational data analysis.
We investigate the respective contributions to the trends by separating the impact on O3 trends of changes in European and local emissions in Sweden, in hemispheric background concentrations (including changes to the top and lateral boundaries) and in meteorology (including changes to biogenic emissions, transport, O3 forming capacity, O3 deposition etc.).
The method for this quantification is described in Sect. 2.5. The methods we
use for evaluation are given in Sect. 2.6.
(a) A flow-chart of the relevant part of the MATCH Sweden
system for this reanalysis study. (b) The total domain of the
European scale model run (pink + light blue) and the domain of the
variational data analysis (light blue).
The MATCH Sweden system
The MATCH Sweden system is an operational system used for annual assessments
of near-surface regional background concentrations in air of O3,
NO2, ammonia (NH3) and sulfur dioxide (SO2) as well as
deposition of sulfur, nitrogen and base cations over Sweden (Alpfjord and
Andersson, 2015). The system includes an operational CTM (MATCH; Multi-scale
Atmospheric Transport and Chemistry; Robertson et al., 1999) and methods for
variational data analysis (using 2DVAR) of atmospheric concentrations in air
and precipitation. The yearly results from the mapping can be found at
www.smhi.se/klimatdata/miljo/atmosfarskemi.
The flow chart in Fig. 1 describes the parts of the MATCH Sweden system that
are used in this reanalysis of near-surface O3 concentrations.
Explanations are provided in Sect. 2.2 to 2.4. For a description of the whole
MATCH Sweden system, see, for example, Alpfjord and Andersson (2015).
First guess – model assessment
The starting point (see Fig. 1) for the two-dimensional
retrospective variational data analysis of near-surface O3 is hourly
fields of modeled O3, produced by MATCH. The MATCH model includes ozone-
and particle-forming photo-chemistry with ∼ 60 species (Langner et al.,
1998; Andersson et al., 2007, 2015). Part of the gas-phase chemical scheme
was updated based on Simpson et al. (2012), except for some reaction rates
(following the recommendations by the International Union of Pure and Applied
Chemistry, IUPAC), and the isoprene chemistry mechanism that was based on an
adapted version of the Carter one-product mechanism (Carter, 1996; Langner et
al., 1998). A selection of compounds with different ozone-forming potentials
is used to represent all hydrocarbons emitted into the atmosphere. The
photolysis rates depend on the photolytically active radiation, which is
dependent on latitude, time of day, cloud cover, etc. In this study MATCH
interpolates the input meteorology and emissions to a domain covering Europe
and surrounding areas with 44 km grid point spacing. MATCH uses all
meteorological model layers for vertical wind calculations, but restricts the
calculations of chemistry and transport to the lower troposphere using the
vertical levels of the meteorological model from the surface up to ca. 5 km
height, which is the model's standard configuration for pan-European
simulations. The selected setup has been demonstrated (e.g., Andersson et
al., 2007; Langner et al., 2012a; Markakis et al., 2016) to be adequate for
describing near-surface O3 across Europe although trends in
stratospheric chemistry or physically driven changes in
stratospheric–tropospheric exchanges will likely not be captured.
MATCH is an offline model and thus driven by meteorological data generated
externally; as such it is often a challenge to undertake long
(multi-decadal) simulations due to non-homogenous input data. Dynamical
meteorological models, which provide the three-dimensional meteorology for
the offline CTMs, are constantly updated to higher resolutions and more
advanced physical schemes. Emission inventories are typically constructed for
certain target years and different methods may have been used to compile
total emissions and/or the geographical distribution of the emissions.
Careless combination of different emission data or meteorology from varying
model configurations can introduce artificial secular trends in the modeling
of atmospheric pollutants. In this study, we specifically aimed for
internally coherent input data, although it led to compromises in, for example, the
temporal coverage of the meteorology and the resolution of the gridded
pan-European emissions. In the following sections we briefly describe the
utilized input data. Further details of MATCH in the present model version
and its ability to simulate near-surface O3 can be found in separate
publications, for example Markakis et al. (2016), Lacressoniere et al. (2016)
and Watson et al. (2015, 2016).
(a) Temporal trend of factors used for scaling boundary
concentration of relevant species (based on Engardt et al., 2017).
(b) Temporal trend of total domain (circles; left vertical scale)
and Swedish (triangles; right vertical scale) annual anthropogenic O3
precursor emissions utilized by MATCH from 1990 to 2013. Emissions of
nitrogen oxides (NOx), non-methane volatile organic compounds (NMVOC) and
carbon monoxide (CO) are indicated by different colors (cf. legend);
emissions of sulfur oxides (SOx) and ammonia (NH3) are excluded from
the panel. (c) Temporal trend of total domain biogenic isoprene
emissions.
Meteorology and boundary concentrations
In the present study we force MATCH with three-dimensional meteorology from
the numerical weather forecast model HIRLAM. Within the EURO4M-project HIRLAM
was run as forecasts from 6-hourly analyses, composed of three-dimensional
variational upper-air analyses and optimal interpolation surface analyses
(Dahlgren et al., 2016). Lateral and lower (sea surface temperature and sea
ice) boundaries were taken from ERA-Interim (Dee et al., 2011). Full
three-dimensional model states needed to run MATCH are available from 1979
through February 2014. Under EURO4M, HIRLAM was running on a domain covering
Europe and northern Africa with 22 km grid point spacing and 60 vertical
layers from the surface to 10 hPa.
Although the present study focuses on Sweden, it is necessary to realistically
describe the fluxes of O3 and its precursors from continental Europe and
further afield. Hemispheric background concentrations of all species for the
modeled year 2000 are similar to the ones used by Andersson et al. (2007).
As in Andersson et al. (2007), boundary values representative of the average
concentrations at the lateral and top boundaries of relevant species are
interpolated spatially with a monthly temporal resolution. Boundary
concentrations of O3, oxidized nitrogen and methane are furthermore
scaled to mimic observed changes in the hemispheric background during the
period 1990 through 2013 following the work of Engardt et al. (2017); see
Fig. 2a. Note that the hemispheric background ozone concentrations are
assumed constant from 2000 onwards following recent assessments of the
evolution of near-surface ozone in Europe (e.g., Cooper et al., 2014). CO and
NMVOC boundaries are held constant throughout the simulation. The same factor
is used for all months of the respective year, although most species also
undergo a seasonal cycle in the boundary concentrations used by MATCH (see
Supplement Fig. S1).
Emissions
The version of MATCH utilized in this study needs anthropogenic emissions of
sulfur (SO2 and sulfate), nitrogen oxides (NO and NO2), carbon
monoxide (CO), non-methane volatile organic compounds (NMVOCs) and
NH3. The model uses annually accumulated values for each species, which
are distributed with different temporal or vertical profiles based on
species and sectors.
For countries outside Sweden (as well as international shipping) we utilize
the gridded (50 km × 50 km) annual data available at the European
Monitoring and Evaluation Programme (EMEP) website (Hjellbrekke and Solberg,
2015; http://www.emep.int; downloaded 23 June 2015). All emission data
were split into congruent 5 km × 5 km cells where we replaced the
coarse-resolution data over Sweden with the original emission data from SMED
(Svensk miljöemissionsdata; http://www.smed.se;
1 km × 1 km converted to 5 km × 5 km cells in EMEP's
geometry). National totals from SMED are very similar to the national totals
available in the EMEP database, but our methodology enables higher-resolution
emission data over Sweden. The gridded 5 km × 5 km emission data
were interpolated to MATCH's 44 km resolution domain during the simulations.
Emissions of biogenic isoprene are calculated online in MATCH following the
E-94 isoprene emission methodology proposed by Simpson et al. (1995).
Both the total domain and Swedish national anthropogenic O3 precursor
emissions decrease strongly over the period 1990–2013 (see Fig. 2b). The
total domain anthropogenic precursor emissions decrease on average
The trend is calculated by linear regression over the period 1990–2013 and
related to the 1990 emission level.
by 1.8, 2.4, 2.6 % yr-1 during
1990–2013 for NOx, NMVOCs and CO, respectively, whereas biogenic isoprene
emissions (calculated online by MATCH) increase by 0.8 % yr-1
according to our simulations (see Fig. 2c). The national Swedish emissions
decrease by similar amounts (2.4, 2.1 and 2.9 % yr-1). The Swedish
contribution to the total domain emissions is 1.0 % for NOx and
1.7 % for NMVOCs and CO on average, with a slight decrease in the
relative Swedish contribution over the period for NOx
(0.01 % yr-1), and a slight increase for NMVOCs and CO (0.01 and
0.003 % yr-1, respectively). The amount and spatial distribution of
the emissions is updated each year.
Instrumentation sites for hourly near-surface ozone concentration
observations in Sweden and Norway, which are used in the variational
analysis. Red circles: sites with full data coverage. Blue circles: sites
with restricted data coverage. The subdivision of Sweden into three regions
(north, central and south) follows county borders, as indicated by the thick
black lines.
Data availability at instrumentation sites for hourly near-surface
ozone concentration observations in Sweden and Norway. Red squares: years
with at least 80 % annual data for sites with full data coverage (see
also Fig. 3). Light red: sites with < 80 % annual data (data
capture indicated in square) for sites with full coverage. Blue and light
blue squares: as for the red squares, but for sites with restricted data
coverage. White squares: no observations are available for that year and site. The LONGTERM reanalysis includes the red measurement sites; the ALL reanalysis includes both red and blue.
(a) Model calculations and scenarios, all covering the
years 1990–2013, including the first guess to the retrospective
variational data analysis and base case to the sensitivity simulations (MFG),
two reanalysis data sets (LONGTERM and ALL) and sensitivity scenarios (MFD, MSE,
MBC and MMET). (b) Formation of contributions to the linear trend
over the period 1990–2013 from the sensitivity simulations (a).
Scenario/data setDescription(a)MFGMATCH base case simulation and first guess used as input to the reanalyses.LONGTERMReanalysis data set of hourly near-surface ozone concentration covering Sweden and Norway based on (1) the MFG European MATCH simulation and (2) selected hourly near-surface ozone measurements in Sweden and Norway, based on temporal coverage of the measurement sites. Optimal for trend analyses. Analyzed andpresented in Sect. 3.ALLReanalysis data set of hourly near-surface ozone concentration covering Sweden and Norway based on (1) the MFG European MATCH simulation and (2) all available Swedish hourly ozone measurements and a selection of the Norwegian (as in LONGTERM). Not used for trend analyses in this study, but best estimate for the hourly near-surface ozone concentration in Sweden at any point in time.MFDMATCH sensitivity simulation where the full domain anthropogenic emissions are kept constant from year to year, set to the level of 2011.MSEMATCH sensitivity simulation where the Swedish anthropogenic emissions are kept constant from year to year, set to the level of 2011.MBCMATCH sensitivity simulation where the top and lateral boundaries for all species are kept constant from year to year, set to the level of 2011.MMETMATCH sensitivity simulation where the meteorology is kept constant, using the meteorological year 2011.(b)SE emisContribution to the trend caused by the change in anthropogenic Swedish emissions, calculated as the model scenario difference: MFG-MSE.FD emisContribution to the trend caused by the change in full domain anthropogenic, non-Swedish emissions,calculated as the model scenario difference: (MFG-MFD)-(MFG-MSE).emisContribution to the trend caused by the change in full domain anthropogenic emissions, calculated as the model scenario difference: MFG-MFD. Used only for the base year sensitivity investigation.boundContribution to the trend caused by the change in lateral and upper boundaries, calculated as the model scenario difference: MFG-MBC.meteoContribution to the trend caused by the variation in meteorology, calculated as the model scenario difference: MFG-MMET.SUMSum of the contributions to the trend, calculated as the sum of SE emis + FD emis + Bound + Meteo.Measurements
Figures 3 and 4 summarize the observations of hourly O3 concentrations
used in the variational analysis and the corresponding hourly data coverage
per year in the period 1990–2013. The Swedish observations were delivered by
the Swedish data host (at the time, 1 July 2017, Swedish Environmental
Institute, IVL). The Norwegian observations were extracted from EBAS
(http://ebas.nilu.no; extracted on 6 July 2017). All sites except Norr
Malma and Rödeby are classified as regional background measurement sites
by EMEP (http://www.nilu.no/projects/ccc/emepdata.html; Hjellbrekke and
Solberg, 2015). Norr Malma is located ca. 70 km northeast of Stockholm and
is considered a regional background measurement site by Stockholm Air and
Noise (http://slb.nu), who are responsible for the site. Rödeby is
located 10 km north of the small town Karlskrona, and is considered a rural
location (Swedish EPA, T. Kyrklund, personal communication, 2015). The sites
included are all instrumentation sites, where O3 is measured
continuously and reported with hourly temporal resolution. The retrospective
variational data analysis is conducted at an hourly resolution, which means
that measurements with a coarser time resolution, such as diffusive samplers,
are not included in the variational technique. Two measurement data sets were
compiled (see Table 1):
The first set includes data from all available instrumentation sites in
Sweden, and a selection in Norway based on data availability, quality and
location. These are all the red and blue sites in Figs. 3 and 4 also
including years where the data capture is lower than 80 %. The reanalysis
based on these measurement data is called ALL.
The second data set includes data from instrumentation sites for which the
data coverage exceeds 80 % for at least 23 out of the 24 years. These are
the red sites in Figs. 3 and 4. The reanalysis based on these measurement
data is called LONGTERM. Råö is seen as the replacement for the site
Rörvik, and therefore these sites form a pair, which is included in this
data set. Birkenes I was replaced by Birkenes II in 2009, and the two sites
were run in parallel for a few years. We choose to include Birkenes II from
2010 and onwards. The reason for the change of site location is that Birkenes
I was influenced by local effects (NILU, S. Solberg, personal communication,
2015). The inclusion of these two sites could introduce an abrupt change in
the reanalysis, but since it is outside the main focus area (Sweden) and
mainly during night we choose to include the site in the LONGTERM reanalysis.
The two measurement data sets are input to two otherwise similar variational
data analyses. The ALL reanalysis is our best estimate of gridded
near-surface O3 over Sweden for a given time. The LONGTERM reanalysis
is used for trend and statistical analyses. We return to whether these
reanalyses differ in Sect. 3.1.
Variational data analysis
The spatial analysis problem can be formulated as how to best distribute
observational information at a discreet number of locations to a spatially
consistent field. We have adopted the 2DVAR approach, which includes a
modeled background field (from a CTM simulation, first guess) combined
with available in situ observations (Robertson and Kahnert, 2007), as
indicated in Fig. 1. With this method the error estimates of both the
background field and the observations play a central role. The observational
errors are assumed independent and uncorrelated, while the background errors
have spatial correlations that form a background error matrix. The solution
is found by the best combination of the background field and observations
given their respective error estimates. This can be described as a
variational problem, defined by a cost function,
J(x)=1/2[x-xb]TB-1[x-xb]+1/2[y-H(x)]TO-1[y-H(x)],
where x is the state to be found (the reanalysis), xb the
background state (our first guess), y the vector of observations,
H is the observation operator and B and O are the
error covariance matrices of the background field and the observations,
respectively. In order to find the optimal solution the cost function is
stepwise minimized by a variational method, starting with x=xb,
and ending with the state x, which represents the optimal balance between
the two terms. During the process the co-variances in the B matrix
acts to extrapolate the observational information in space.
We restrict our study to reanalyze near-surface O3 on the regional
background scale, which means we only include regional background measurement
sites. We also restrict our study to 2DVAR, rather than using higher-dimensional variational analysis. The background covariance matrix is
modeled in a simplified fashion with a constant background error, 20 times
larger than the observation error, and Gaussian spatial correlations with a
length scale of 1000 km. This implies a strong weight towards the
observations and assumes a rather large horizontal influence of the
observations.
The variational data analysis was conducted on a 22 km resolution grid with
hourly temporal resolution, combining the modeled first guess for
near-surface O3 (the MATCH base case scenario, MFG in Table 1) and
regional background measurements. Two 24-year reanalyses were formed using
the two different sets of hourly measurement described in Sect. 2.3 (ALL and
LONGTERM in Table 1). If an included measurement site was lacking an
observation for a specific hour, the site was excluded from the variational
data analysis for that specific hour.
The resulting spatially resolved hourly O3 data are used to form annual
and seasonal statistical metrics for O3, such as the mean value and the
maximum 1 h mean value, and annual policy- and impact-related metrics (see
Fig. 1). We analyze these annual and seasonal data for the 1990–2013 mean,
trend and extreme values in Sect. 3.2 (annual/seasonal mean and maximum) and
Sect. 3.4 (health and vegetation impact metrics).
Understanding the trends
We also include a quantification of the causes to the trend in near-surface
O3 concentration. For this investigation we conduct model simulations
with MATCH, excludingvariationaldata analysis. We investigate the respective contributions to the modeled
total trend due to the following:
Change in emissions, which is separated between (1) Swedish anthropogenic emissions (SE emis) and (2) full domain (see Fig. 1b) non-Swedish anthropogenic emissions (FD
emis).
Change in lateral and upper boundaries (bound).
Change in meteorology, including online modeled biogenic isoprene emissions
(meteo).
Four sensitivity simulations are conducted, in which each of the four listed
factors are kept constant at their level in 2011. The respective
contributions presented in Sect. 3.3 are formed through the following
sequence: (1) calculation of gridded metrics (focusing on monthly 1 h maximum,
monthly mean and annual 1 h percentile levels); (2) calculation of secular
gridded trends over the monthly or annual metrics; (3) calculation of
regional (north, central, south; see Fig. 3) mean of the secular trends; and
(4) calculation of the difference between the regional mean trend in MFG and
the corresponding sensitivity simulation. All model simulations and scenarios
are described in Table 1a. The method of forming the contributions from these
simulations is shown in Table 1b.
There are three critical points in the investigation of the causes of the
trend: first, this quantification methodology assumes linearity, whereas the
sum of contributions (SUM) is not necessarily equal to the trend in the MFG
simulation. If they are not equal, it means that the simulations are
non-additive. This could occur when changes to mixtures of complex
chemistry, weather situations and emissions take place, or as a numeric
effect in the model. For this reason we compare the sum of the trend in the
estimated contributions to the MFG trend. Second, our methodology quantifies
the contributions to the trend in the MFG simulation, which may differ from
the reanalyzed trend. Thus we will compare the reanalyzed trend and the MFG
trend to make sure the base case simulation does not deviate too strongly
from the reanalysis results. If the deviation is large, i.e., the modeled
trend is far from the observed, it means that the MFG simulation is
non-representative. Such discrepancies could arise from over-sensitivity in
MATCH to one process and insensitivity to another, compared to the real
world, or imperfections/artificial trends in the input data such as
erroneously estimated emissions or erroneous assumptions on the trend in
hemispheric background concentrations. If either is true (non-additive or
non-representative) for the trend in a specific metric, then our method
cannot be used to explain that specific trend. Third, the attribution may be
sensitive to the chosen base year. Sensitivity simulations using 1990 as
base year instead of 2011 are also conducted, to investigate the robustness
of the results. As investigating all 24 years as base years would take too
much computational effort, we choose 1990 as it differs from 2011 both for
European emissions and climatologically.
The NAO index was high in
early winter 1990 and low in 2011, whereas the summer index was positive but
close to 0 in 1990 and negative in 2011.
If the contributions to the trend
differ too much between the base years 1990 and 2011 then the results are
not robust. If they are similar it is not a guarantee that the results are
robust but it is an indication. The contributions with 1990 as base year are
formed in the same way as for the 2011 sensitivity runs. The contributions
due to change in top and lateral boundaries (bound) and variations in
meteorology are included in the same manner, while we compare the total
footprint of the change in emissions, i.e., the sum of FD emis and SE emis
(emis) rather than the two parts.
Evaluation
We evaluate two aspects of the reanalysis. The first is an independent
evaluation for a single year with focus on the variational data analysis
method. The second is an evaluation of the simulated near-surface O3
concentration trend over the 24-year period and our ability to explain the
causes of the trend.
For an independent evaluation of the variational data analysis method we
conduct a cross validation at the included Swedish measurement sites. In this
method we exclude one measurement site at a time from the variational data
analysis, and evaluate the results at the excluded location. This means we
conduct one 2DVAR simulation for each considered measurement site. Due to the
large amount of computation involved we evaluate only one of the years using this
method. We choose the year 2013, which is when the data coverage is the
largest. This means that we also have the opportunity to investigate whether
we see any difference in performance between the reanalysis with the larger
number of measurement sites (ALL) and the long-term reanalysis (LONGTERM).
The evaluation metrics used here are mean value (mean), standard deviation
(σ), model mean bias normalized by the observed mean (%bias),
Pearson correlation coefficient (r) and the root mean square error (RMSE);
see Supplement Sect. S1.
For the evaluation of the long-term trend we focus on the three critical
points raised in the previous section: (1) the additivity of the trend in the
contributions as compared to the trend in O3 concentrations from the MFG
simulation, (2) whether the MFG trend is representative of the O3
concentration trend in the LONGTERM reanalysis results and (3) whether the
contributions to the secular trend are sensitive to base year. We focus this
investigation on 11 different percentiles of hourly mean O3
concentrations, for an estimate of the scores at different concentration
levels. We focus specifically on averages over the three Swedish regions
north, central and south (see Fig. 3), to investigate whether there is any
variation in performance in Sweden.
Additional evaluation and comparisons of the temporal variation over the
whole period is included in the Supplement for the two reanalyses LONGTERM
and ALL, the MATCH simulation MFG and observed annual mean (see Supplement
Sect. S2, Figs. S2–S4 and Table S1).
(a, b) Temporal trends in annual percentiles of hourly mean
near-surface ozone (levels: 0, 2, 5, 10, 25, 50, 75, 90, 95, 98 and 100; see
Table S2) averaged for the three regions: north (blue), central (green)
and south (magenta) for the sum of the contributions to the trend (SUM) vs.
the MATCH model simulation MFG (a) and the reanalysis LONGTERM vs.
the MATCH model simulation MFG (b). Filled circles indicate
significant trends (p≤0.05) in the MFG simulation, whereas
non-significant MFG trends (p>0.05) are indicated by an empty
circle. (c) Sensitivity in contributions to the secular trends in
regionally (north: squares; central: circles; south: diamonds) averaged
annual percentiles (levels: 0, 2, 5, 10, 25, 50, 75, 90, 95, 98 and 100) of
hourly near-surface O3 over the period 1990–2013 due to choice of base
year (1990 vs. 2011). Modeled contributions to the near-surface ozone trend
due to change in top and lateral boundaries of relevant species (dark yellow,
bound), change in full domain emissions (blue, SE emis + FD emis) and
variation in meteorology (light yellow, meteo). 1:1 line in black, factor 2
lines in dark grey.
Evaluation of modeled hourly and daily maximum of 1 h mean
near-surface ozone concentrations in 2013 at Swedish observation sites. Mean
value (mean), standard deviation (σ), model mean bias normalized by
the observed mean (%bias), Pearson correlation coefficients (r) for data
including at least 10 pairs, the root mean square error (RMSE) and number of
observed hours/days* at the sites. The evaluation includes the reanalyzed data
sets ALL and LONGTERM, where ALL is evaluated at the 12 Swedish sites
included in that simulation, and LONGTERM is evaluated at the 6 Swedish
sites included in that simulation (see Fig. 4). For each of these data set
evaluations we include the observation-dependent reanalysis (2DVAR), the observation-independent cross validation of the reanalysis (cross) and the MATCH base case
simulation (MFG). The top half of the table shows the temporal performance
(spatial mean of evaluation statistics; see Supplement Sect. S1). The bottom
half of the table shows spatial performance (spatial statistics of annual
means; see Sect. S1).
Spatial mean of evaluation statistics Hourly meanmeanSD%biasrRMSE#hours(ppb(v))(ppb(v))(%)(ppb(v))ALLobs30.911.08760MFG31.19.41.40.678.8cross30.69.9-0.30.768.02DVAR30.811.1-0.60.943.5LONGTERMobs32.610.58760MFG31.29.7-3.30.678.7cross32.29.3-0.10.728.52DVAR32.610.70.20.972.7Daily maximummeanSD%biasrRMSE#days(ppb(v))(ppb(v))(%)(ppb(v))ALLobs39.48.7365MFG37.77.6-4.30.795.8cross38.37.9-2.60.835.22DVAR39.58.50.30.971.4LONGTERMobs40.08.7365MFG37.88.0-5.60.796.0cross38.77.9-3.30.815.62DVAR40.48.91.01.000.8Spatial statistics of annual means Annual meanmeanSD%biasrRMSE#stns(ppb(v))(ppb(v))(%)(ppb(v))ALLobs30.92.512MFG31.11.20.60.213.0cross30.61.8-1.00.113.52DVAR30.82.8-0.50.980.7LONGTERMobs32.62.26MFG31.21.0-4.1X3.4cross32.21.6-1.2X4.32DVAR32.62.20.2X0.2Annual meanmeanSD%biasrRMSE#stnsof daily(ppb(v))(ppb(v))(%)(ppb(v))maximumALLobs39.41.312MFG37.71.2-4.40.432.5cross38.31.6-2.70.312.32DVAR39.51.60.30.901.0LONGTERMobs40.01.66MFG37.81.4-5.7X2.9cross38.72.0-3.4X2.72DVAR40.41.61.0X0.4
* A daily data coverage
of 75 % (> 18 h) is required to include the observed daily
maximum as a valid observation.
ResultsThe performance of the model simulations and reanalyses
Before turning to the evaluation results, we investigate whether the two
ozone reanalyses differ. We do this by comparing time series of annual
O3 metrics for the two data sets. The investigation is presented in the
Supplement and shows deviations in the later years as the number of sites
in the ALL data set increases beyond the sites included in the LONGTERM data
set (see Sect. S3 and Figs. S2–S4). The deviation in annual mean
near-surface O3 concentrations is larger than for annual maximum 1 h
mean given that many of the newer sites are sensitive to nighttime
inversions. Due to the visible deviation in results, we use the LONGTERM for
the trend and statistical analyzes in the paper, whereas both are used for
the evaluation of the 2DVAR method in this section. Both are included in the
method evaluation because the evaluation scores may be dependent on the
density and specific locations of the measurement sites. The ALL data set is
to be used as a best estimate of geographically resolved near-surface O3
concentrations for Sweden for a subset period within the full period
1990–2013.
In Table 2 we show the evaluation statistics from the validation of hourly
and daily maximum of hourly mean near-surface O3 in 2013. The
near-surface O3 concentrations from the MFG simulation compare well with
observations, and the 2DVAR technique leads to improvements. The spatially
averaged correlation coefficient of hourly near-surface O3
concentrations (see Sect. S1 increases from 0.67 when comparing the MFG
O3 concentrations to observations, to 0.76 when comparing the ALL
reanalysis independently to observations through a cross validation
(Table 2). The %bias decreases from 1.4 to -0.3 and the RMSE is also
improved in the independent evaluation of the ALL reanalysis. Similar
improvements are also obtained when using fewer measurements (LONGTERM,
Table 2), showing that the method is stable with the number of measurement
sites. The cross validation spatial error (RMSE) is however larger than that
obtained when evaluating the MFG simulation against independent observations,
where the cross validation results indicates that the 2DVAR reduces the
quality of the annual mean spatial variation in 2013. The evaluation of the
daily maximum generally shows better correlation but slightly larger bias
than the evaluation of the hourly mean. The spatial correlation is also worse
in the cross validation compared to the MFG, but the spatial error is
improved. Overall, the independent cross validation shows that the 2DVAR
method improves the performance of the modeled hourly mean and daily maximum
O3 compared to the MFG simulation. This is true not only in the
measurement sites, but also elsewhere, with exception of the spatial
variation.
Seasonal cycle of monthly mean (a) and monthly maximum
(b) of 1 h mean near-surface ozone concentrations averaged over the
period 1990–2013 (circles; left vertical scale) and region (north, central
and south Sweden; see Fig. 3) and the linear trend over the same period of
the respective spatially averaged monthly values (triangles; right vertical
scale). The different regions are identified by color; see legend.
Results from the LONGTERM reanalysis.
Statistical properties of the annual mean (top row; a–e)
and annual maximum 1 h mean (bottom row; f–j near-surface ozone
concentration. In the columns from left to right: 1990–2013 mean (a, f), 1990–2013 maximum (b, g), 1990–2013 standard deviation
(c, h), linear trend over the period 1990–2013 (d, i) and
significance in the linear trend over the period (e, j). Results
from the LONGTERM reanalysis.
In Fig. 5 we compare regionally averaged linear trends in annual percentiles
(levels: 0, 2, 5, 10, 25, 50, 75, 90, 95, 98 and 100) of hourly near-surface
O3 over the period 1990–2013 for the MFG simulation, the LONGTERM
reanalysis, the sum of contributions and the contributions to the trend for
different base years. Investigating the additivity of the four contributions
(bound, meteo, SE emis and FD emis), we compare the O3 concentration
trends in the MFG simulation to the trend in the sum of the contributions
(SUM, Fig. 5a). Almost all values fall close to the 1:1 line. Only a few of
the very weakest O3 trends fall outside the factor 2 lines. Thus, the
contribution experiment can be used to explain the MFG O3 trend.
Comparing the LONGTERM and MFG trends in near-surface O3 (Fig. 5b), the
values are within a factor of 2 for most percentiles and regions. There is a
general tendency for the positive MFG trends to be stronger than the
reanalyzed trend (LONGTERM). The largest deviations in the O3 trends are
in the north, and the relationship between these two is not as linear as in
the other two regions. Most of these trends are however not significant. This
demonstrates the added value of the measurement model fusion, where errors in
the modeled trend are corrected by the analysis. The deviations are small
enough to conclude that in most cases the MFG is representative, showing that
the MATCH model can be used to understand the trends in the LONGTERM data
set. Finally, investigating the impact of the selected base year in the
sensitivity simulations, we compare the contributions (bound, emis and meteo)
based on keeping the year 2011 constant in the sensitivity simulations to
keeping the year 1990 constant (Fig. 5c). Most contributions to the trend in
percentiles are robust (Fig. 5c), falling close to the 1:1 line. Only a few
of the very weakest O3 contribution trends fall outside the factor 2
lines (for the meteo contribution). The contributions to the secular trend in
some of the monthly mean and the monthly maximum 1 h mean near-surface
O3 differ more for the two base years than the percentiles (Fig. S5).
For monthly mean the trend due to changes in meteorology is stronger for some
months (one month is weaker) when 2011 is used as base year compared to 1990.
The other contributions fall within the factor of 2 lines. For monthly maximum
the deviation is larger, even differing in sign for the contribution due to
variation in meteorology for some months, and a few contributions due to
emission change also fall outside the factor of 2 lines.
Temporal variation of annual percentiles of near-surface ozone
concentrations averaged over the three regions: north (a), central
(b) and south (c) of Sweden (see Fig. 3). The line marked 0
is the zero-percentiles (lowest hourly mean near-surface ozone concentration
of the year), 100 represents 100th percentile (highest hourly mean near-surface ozone
concentration of the year), 50 is the 50th percentile (i.e., annual median of
the hourly mean near-surface ozone concentration). The sign of the
corresponding linear trend (see Table S3, including a statistical analysis of
the trend) of each percentile is indicated by color: a negative linear trend
over 1990–2013 is indicated by grey symbols; a positive trend by orange
symbols. Statistically significant trends (p≤0.05) are indicated by
thick lines. Results from the LONGTERM reanalysis.
Linear trend over 1990–2013 in monthly mean (a, c) and
monthly maximum 1 h mean (b, d) near-surface ozone concentration
for the north (a, b) and the south (c, d) Sweden regions
(see Fig. 3). Reanalyzed (white diamond; LONGTERM reanalysis) and modeled
first guess (MFG) near-surface ozone trend (brown diamond), and modeled
contributions to the near-surface ozone trend due to change in emissions:
anthropogenic Swedish (dark blue, SE emis) and full domain, non-Swedish
(light
blue, FD emis) emissions; trend in top and lateral boundaries of relevant
species (dark yellow, bound) and variation in meteorology (light yellow,
meteo). The sum of the modeled contributions is indicated by the dashed
brown line.
In conclusion we have shown that the MFG performs well for hourly
near-surface O3 concentrations and the 2DVAR analysis improves the
performance to almost perfect correspondence to the measurements in the
measurement locations, and improved performance elsewhere (see the
cross-validation), with the exception of the spatial variation. There is
added value of a reanalysis when investigating the trend of near-surface
O3 concentrations. The MATCH model can be used to investigate the
causes to the reanalyzed O3 trend, but the contribution of
meteorology to the monthly maximum is not robust under the choice of base
year for all months. In the north the trends in the reanalyzed and the MFG
O3 concentrations deviate by more than a factor of 2 for some
percentiles. We will focus on this deviation more in the final discussion
(Sect. 4).
Reanalyzed near-surface ozone in Sweden 1990–2013
The mean 1990–2013 seasonal variations in monthly mean and monthly maximum
of 1 h mean near-surface O3 are presented in Fig. 6, averaged over the
three regions: north, central and south (as defined in Fig. 3). The seasonal
variation in the linear trend of the spatially averaged monthly values is
also included in the figure. Spatially resolved statistics for annual mean
and annual maximum of 1 h mean near-surface O3 are provided in Fig. 7.
The temporal evolution of 11 percentile levels from the 0th (annual minimum
1 h mean) to the 100th (annual maximum 1 h mean) are shown in Fig. 8, and
the corresponding trends with indication of significance levels are
recaptured in the Supplement (Table S2).
1990–2013 period statistics
The near-surface O3 in Sweden exhibits a seasonal variation, which peaks
during spring (Fig. 6). In the north the seasonal maximum concentration
occurs in April, whereas it occurs later, in May, in the regions further
south. The earlier peak in the north, as compared to the south, was also
shown by Klingberg et al. (2009) for in situ observations. In the north, the
seasonal peak in monthly mean O3 concentrations is higher than the
corresponding seasonal peaks in the other two regions, and this is a feature
throughout the whole winter half-year: the monthly mean O3
concentrations are higher in the north than the more southerly regions during
October–April. During the summer, the monthly means are higher in the south
than in the other two regions. This leads to a 24-year period mean value
(Fig. 7) that is highest in the northerly mountains and lowest in central
Sweden. This pattern is also supported by Klingberg et al. (2009) based
purely on observations, but including a larger number of observation sites
through the inclusion of passive diffusion samplers.
For the period mean seasonal variation in monthly maximum 1 h mean
near-surface O3 (Fig. 6b), there is a similar seasonal peak in
April–May, but there is also a secondary peak during summer (in August). The monthly maximum 1 h mean near-surface
O3 during March–October becomes higher when looking further south. This applies to both the primary and the
secondary seasonal peaks in monthly maximum. The 24-year period mean of the
annual maximum of 1 h mean near-surface O3 (Fig. 7) is lower in central
Sweden than in the south and the north, and it is highest in the south.
The lower period mean of the near-surface O3 in the south than in the
north is mainly caused by the higher altitude of the latter, mountainous
region, whereas the opposite gradient for the annual maximum 1 h mean is caused
by the distance to continental Europe, where the high-ozone events originate
from. The difference in spatial patterns between the southern, central and
northern parts of Sweden is why we choose the three regions defined in
Fig. 3. The period maximum of the annual means and period maximum 1 h mean
near-surface O3 concentrations have similar spatial variation as their
respective period means (Fig. 7) The overall 24-year maximum 1 h mean
near-surface O3 reaches above 240 µg m-3 in isolated
parts of the south, and is generally above 180 µg m-3 in the
south and 130 µg m-3 in the central and northern part of
Sweden.
Linear trends over 1990–2013 in annual percentiles of hourly mean
near-surface ozone concentrations for the north (a) and the south
(b) Sweden regions. Reanalyzed (white diamond; LONGTERM reanalysis)
and modeled MFG near-surface ozone trend (brown diamond) and modeled
contributions to the near-surface ozone concentration trend due to change in
emissions: anthropogenic Swedish (dark blue, SE emis) and full domain,
non-Swedish (light blue, FD emis) emissions; trend in top and lateral
boundaries of relevant species (dark yellow, bound) and variation in
meteorology (light yellow, meteo). The sum of the modeled contributions is
indicated by the dashed brown line.
Trend over the period
Seasonal variations are also present in the trend of both monthly mean and
monthly maximum 1 h mean near-surface O3 concentrations (Fig. 6). Monthly
means increase strongly during winter and spring (approx. November–April),
and decrease moderately (north) or strongly (central and south) during summer
(May–August). The trends in monthly maximum 1 h mean follow a similar
pattern. Generally, the rate of change is stronger or at the same level in
the central and south as compared to the north. The strongest decrease is in
the August maximum 1 h mean in the south and central, and the strongest
increase is in the March monthly mean in the central and north.
The annual mean near-surface O3 (Fig. 7d, e) increases almost everywhere
in Sweden over the time period. The trend is however only significant in
restricted parts of central and south regions, due to considerable
interannual variation in the areas with the highest trend. The annual
maximum 1 h mean near-surface O3 (Fig. 7i, j) significantly decreases
in south and central regions, whereas the change in the north is a mixture of
increase and decrease, and it is without significance in most areas.
We proceed by investigating the trend in annual percentiles (Fig. 8) of
hourly near-surface O3 concentration, averaged
The percentile
is calculated per grid square for all hours in each year, then regional mean
annual percentiles are calculated and finally the trend is calculated based
on these averaged percentiles.
over the three Swedish regions (see Fig. 3).
In all three regions the low and medium percentiles increase, while the
highest percentiles decrease from 1990 until 2013. This was also shown
by Simpson et al. (2014) based on observations for northern Europe and based
on observations for Europe, US and East Asia by Lefohn et al. (2017).
Further, using hourly O3 observations, Karlsson et al. (2017) showed
that reduced concentrations in northern Europe were restricted to the highest
O3 concentrations during summer daytime, while the increase in low and
mid-range concentrations occurred during wintertime at both day and night.
In central and south regions the decrease in the highest near-surface O3
percentiles are significant and stronger than in the north, and this decrease
is evident throughout the maximum 10 % percentile range (although the
change is not significant for the 90th and 95th percentile levels; see
Fig. 3). This change is mainly caused by decreased high values during the
summertime. In the north, only the annual maximum 1 h mean decreases and
the interannual variability is stronger than the rate of change, indicated
by the lack of significance for this percentile. The medium and low
percentile increase in the north is moderate, but significant, for most
percentiles up to the 95th, with very similar rates of change. In the central
and south the change in the low percentiles is highly significant and
stronger than in the north. This is an indication that the increase in low
near-surface O3 concentrations cannot only be explained by increasing
background. As a result of the decrease in high and increase in low
percentiles, there was a narrowing of the range of the near-surface
O3 concentrations over the period. This was also observed in the UK by
Jenkin (2008) for 1990 until the early 2000s and in the US by Simon et
al. (2015) for 1998–2013, both studying urban and regional background
measurements across the respective countries. Jenkin (2008) interpreted it as
caused by three major influences: (i) increasing hemispheric background,
(ii) decreasing severity in high-ozone events arising from the European
continent and (iii) decreasing local-scale removal of ozone due the control
of NOx emissions. Simon et al. (2015) interpreted the US evolution as a
response to the substantial decrease in O3 precursor emissions in the US
over the time period. Decreased primary NO emissions results in decreased
O3 titration close to combustion sources, but also reduces local O3
further away from the emissions sources when there is little photolysis
(especially in the winter and during nighttime). In the next section we
investigate the impact of Swedish and European emission decrease over the
period, and relate this to the impact of change in the chemical composition
of the hemispheric background and meteorological variations.
Attribution of the change in near-surface ozone
In this section we quantify the contributions of physical factors to the
modeled trend of near-surface O3 concentrations in Sweden during the
period 1990–2013. We investigate the impact of the trend in lateral and
upper boundaries, meteorological variations and Swedish and European (i.e.,
full domain, non-Swedish) anthropogenic emission change. In Figs. 9 and 10
the contributions to the trend in seasonal variations and percentiles are
delineated for the north and south regions.
We start our attribution by analyzing the impact of changing hemispheric
background levels of relevant chemical species (“bound” bars in Figs. 9 and
10). These contribute to an increase in monthly mean and maximum 1 h mean
throughout the year and for all percentiles, mainly as a result of our
assumption of an increasing O3 concentration trend in the lateral and
upper boundaries during the 1990s and constant boundary conditions for
O3 during the rest of the period. There is a seasonal variation in the
trend of the boundary contribution, with lower impact during summer. This
variation is likely a result of an O3 destruction process that is
stronger during summer than winter, such as dry deposition to vegetation and
photolysis of ozone. The seasonal variation in the contribution to the trend
from the boundary impacts both monthly mean and maximum 1 h mean. Our
representation of the trend in the concentration of species at the model
domain boundary is climatological. The climatological upper boundary means
that the interannual variations in near-surface O3 are likely
underestimated in remote locations. The impact on interannual variations may
be largest at high altitudes or far away from the major anthropogenic
sources. Hess and Zbinden (2013) showed the importance of the stratospheric
contribution to the interannual variation at Mace Head and Jungfraujoch; it
is possibly also important in the north of Sweden, especially in the
mountainous areas. Such variation is not captured by the boundary settings,
but it is indirectly included in the reanalyses data sets through the
variation in the measurements included in the variational data analysis. As a
consequence, the MFG and “bound” simulations underestimate the interannual
variability as compared to observations and the reanalysis (see Table 2), and
this could also affect the “bound” trend.
The impact of meteorological low-frequency variations (“meteo”) during the
24 years is also an important factor, but more difficult to interpret. The
meteorological variation causes a positive trend in near-surface
O3 concentrations for most monthly means and maxima, as well as for most
percentiles. Note the shift from a generally strong positive contribution to
a strong negative contribution from the 98th percentile to the
100th percentile in the south. The meteorological influence on the trend is
as large as the impact of the change in boundary for most percentile levels
in the south, while it is weaker for most percentile levels in the north.
Linear trend during 1990–2013 of policy-related metrics in the
three Swedish regions north, central and south (see Fig. 3). Asterisks (*,
**, and ***) indicate that the trend is significant (p≤0.05,
p≤0.01, p≤0.001, respectively).
MetricsNorthCentralSouthMean (µg m-3 yr-1)+0.18*+0.13+0.18*SOMO35 (ppb(v) d yr-1)+14-3.1-4.7Maximum 8 h mean (µg m-3 yr-1)-0.11-0.68**-1.2**Maximum 1 h mean (µg m-3 yr-1)-0.14-0.82**-1.4***AOT40c (ppm(v) h yr-1)-0.01-0.07*-0.09AOT40f (ppm(v) h yr-1)+0.03-0.09-0.12*#hours > 80 µg m-3 (# yr-1)+26*+1.7+6.6#days > 70 µg m-3 (# yr-1)+1.3+0.73+1.1#days > 120 µg m-3 (# yr-1)+0.01-0.12*-0.32**
During the period 1990–2013 both European (full domain, non-Swedish) and
Swedish emissions decreased strongly. There is a strong seasonality in
the impact of the decreasing European emissions, and the contribution to the
trend of the Swedish emissions follows the same pattern but with smaller
magnitude (see Fig. 9, “FD emis” and “SE emis”, respectively). During
summer the decreasing emissions acted to lower both the monthly mean and
maximum 1 h mean. During winter the trend in monthly maximum 1 h mean is
unaffected by the change in emissions, indicating that the highest
near-surface O3 concentrations during winter are due to sources other
than local O3 production. Emission decreases caused increases in monthly mean near-surface O3 concentrations in the winter,
due to reduced O3 destruction by primary NO emission. Trends in
percentiles (Fig. 10) show that the emission decrease caused decreases
to percentiles higher than the 50th level, and increases below. The impact is
stronger in the south than in the north, which is expected due to the south
being closer to the European continent. The contribution of the trend in
emissions is often stronger than the changing boundary, e.g., in the south for
most percentiles and for monthly maximum 1 h mean during the summer half-year
in both regions. Thus, the observed increase in low and medium near-surface
O3 levels is caused by a mixture of both changes to the hemispheric
background levels and emission reductions of O3 precursors, while the
decrease in the high percentile levels is mainly caused by emission decrease.
Implications for health and vegetation impacts
For the protection of vegetation, the target value by EU (EU directive
2008/50/EC) states that the 5-year mean AOT40 (near-surface O3 concentrations above 40 ppb(v) accumulated over May–July; AOT40c) must not
exceed 9 ppm(v) h, and as a long-term goal AOT40c must not exceed
3 ppm(v) h during a calendar year. For protection of human health the
target value by EU (EU directive 2008/50/EC) states that the daily maximum
running 8 h mean near-surface O3 concentrations must not exceed
120 µg m-3 for more than 25 days per year as a 3-year mean, and
as a long-term goal the daily maximum of 8 h mean near-surface O3 concentrations must not exceed 120 µg m-3 at all. Sweden has
formulated 16 environmental quality objectives, including clean air,
alongside specifications to help reach these objectives. The following
specifications are currently valid for near-surface O3 concentrations in
Sweden (NV, 2015): the hourly mean must not exceed 80 µg m-3,
the daily maximum 8 h mean must not exceed 70 µg m-3 and
AOT40f (O3 concentrations above 40 ppb(v) accumulated over
April–September) must not exceed 5 ppm(v) h. SOMO35 (the sum of ozone
means
For SOMO35 the mean is defined as the daily maximum of
running 8 h mean near-surface O3 concentrations and the accumulation is
over a year unless otherwise is stated.
over 35 ppb(v)) is used as a metric
describing human exposure. The cut-off value of 35 ppb(v) is often used in
risk assessments as a statistically significant increase in mortality has
been observed at daily ozone concentrations > 25–35 ppb(v)
(Bell et al., 2006; Amann et al., 2008; Orru et al., 2013). In Table 3 we
present the linear trends in our reanalysis data set for these metrics, and
have collected geographically resolved statistics, such as the period mean,
maximum and linear trend in the Supplement (Figs. S6–S10).
The narrowing of the O3 concentration range, especially through
increasing lower percentiles, can impact human and vegetation exposure to
O3. The effect metrics based on accumulation of values above a threshold
(AOT40c; AOT40f; SOMO35) and the number of days with daily maximum of 8 h
mean near-surface O3 concentrations exceeding 120 µg m-3
have decreased over the period in the south and central regions, as
they have the highest values in the year. This is in agreement with the decrease
in the highest percentiles in these regions (see Table S2). Conversely, the
metrics with lower threshold values increase, such as the number of hours
exceeding 80 µg m-3 and the number of days with daily maximum
8 h mean near-surface O3 concentrations exceeding
70 µg m-3. This increase is significant in the north, whilst
it is not significant in the south and central. This agrees with the change
in medium and low percentiles. A continued increase in low values would cause
a continued increase in these metrics, and would eventually reverse the
decreasing trend to an increase. This is valid specifically for those metrics
with accumulation of values or higher thresholds, such as SOMO35 and AOT40c.
Discussion
This work improves upon previous studies by investigating the trends in
near-surface O3 concentrations via a combination of both observed and
modeled data The respective advantages of modeling (geographical and
temporal coverage) and observations (the most reliable O3 concentration
estimate at a discreet point) can be exploited through variational data
analysis to reach a greater understanding of the atmospheric state, and the
model can further be used as a tool to explain what is described.
Our results should, however, also be viewed in the context of their
limitations. The length scale of the variational data analysis is set to
1000 km, implying a large horizontal influence of the observation
increments. This is related to the sparse network of regional background
observations but also to the relatively small emissions of O3 precursors in
Sweden resulting in weak horizontal gradients of near-surface O3 on the
regional background scale. The large length scale is also a filtering of
local influences in the observations, consequently suppressing sharp
gradients in the analysis. However, the horizontal variation in near-surface
ozone is larger in the south than in the north, and the large length scale
chosen in the data analysis may cause too-weak horizontal gradients in the
reanalysis data set, especially in the south. An improvement to this would be
to describe the geographical variation of near-surface ozone in the
background error field, rather than representing this with a constant value as
done in this study. The model simulations have a relatively coarse horizontal
resolution, meaning that processes that are more local in origin are not
captured by the model – these include the role of local topography or
coastal climate for the nighttime boundary layer stability (Klingberg et
al., 2011), or local emission sources. As a result, the variational data
analysis scheme will spread such features to parts of the model results where
they are not valid. Some of the southerly sites in the variational data
analysis are known to experience nighttime inversions with associated
depletion of near-surface O3 and the reanalysis will thus be affected by
this. Introducing a geographically varying length scale and background error
in the variational data analysis and an improvement in the spatial resolution
of the model would improve the spatial representation of the analysis, the
latter since the difference between observation and model has the potential
to decrease at these observation sites.
As with all modeling studies, the model cannot perform better than the
quality of the forcing input data. Knowledge of emissions in the beginning of
the 24-year period is less comprehensive than at the end, which could
introduce artificial trends to the MFG. The trends in lateral and top
boundary conditions are taken from the work by Engardt et al. (2017) and are
based on observed trends at regional background location in Europe. The upper
boundaries are especially poorly constrained in our study, and as a
consequence so is the stratospheric contribution to the interannual
variation and trend. The variational data analysis reduces the impact at the
surface caused by errors in the lateral and upper model boundaries. However,
the reanalysis may still be affected in regions with sparse measurement
coverage. This can affect the attribution to the trend. In this study the MFG
simulation captures the observed (reanalyzed) trend reasonably well, but
there is a discrepancy between the reanalysis and MFG trend for most
percentile levels in north Sweden. To investigate this in more detail, we
have compared the error in trend by percentile (the difference between the
trends in MFG and LONGTERM) to the trend caused by the four contributions
(bound, meteo, SE emis and FD emis). The resulting figure is included in the
Supplement (Fig. S11). There is a 1:1 relation between the impact of the
trend in the European emissions and the deviation between the MFG and the
LONGTERM trends. This could be caused by overestimation of the European
emissions trend. A similar tendency is seen for the Swedish emission
contribution in the central and south regions. This calls for emission
inventories to be improved in order to assure the trend in ozone precursor
emissions is correct. Another reason for this could be the too-strong model
sensitivity to the European emission trend in the north. If this was true, it
would have implications for sensitivity studies that consider the future
development of near-surface O3. In studies relating the impacts of
future climate change to future anthropogenic precursor emission change, a
robust conclusion for most models is that the impact on annual or summertime
mean near-surface O3 concentrations of future precursor emissions is much
stronger than the impact of climate change (e.g., Engardt et al., 2009;
Langner et al., 2012b; Watson et al., 2016). If models are too sensitive to
trends in emissions in remote areas, compared to other processes, such a
conclusion might change. Parrish et al. (2014) also compared observed and
modeled trends and found that the three chemistry–climate models studied
failed to reproduce the observed trends – the modeled O3 concentration
trend was approximately parallel to the estimated trend in anthropogenic
precursor emissions of NOx, whilst observed O3 concentration
changes increased more rapidly than these emission estimates. This implies
that there is a lack of knowledge relating to controls of concentrations of
tropospheric O3. The question of whether it is the trend in ozone precursor emissions or
the model sensitivity to emissions which needs improving is left for future
studies.
Our study shows that the impact of meteorological variability on the trend
changes strongly from lower percentile levels to the very highest (in the
south), with a shift from a positive to a negative contribution (see
Fig. 10). Thus, conclusions drawn on the importance of meteorological
variability in comparison to other factors such as changes in emissions will
vary strongly depending on the metric that is studied. We have also studied
the impact of base year in the sensitivity study (1990 vs. 2011; see Figs. 5c
and S5). The attribution to the trend is robust for all percentiles,
including the annual maximum, whereas the monthly maximum is not robust for
emissions and meteorological variation. So far studies of the future
development of near-surface O3 have focused on long-term means such as
summer mean (e.g., Langner et al., 2012a, b; Watson et al., 2016), whereas
the direction of cause of high-frequency metrics, such as the higher
percentiles we show here, have not been established and should be
investigated further.
Finally, we conducted a trend analysis of the reanalyzed near-surface
O3 using linear regression. We have chosen to present the trend in the
LONGTERM data set in all analyzes, regardless of whether it is statistically
significant or not. We stress that a trend contains valid information even
where it is not statistically significant – and it will become significant
if the change and variability remains the same over time. We also recognize
that there are other methods of investigating the statistical behavior of
the data set, and therefore welcome further use of the data, which may be
provided upon request to the corresponding author.
Conclusions
We constructed two hourly reanalyses of near-surface O3 for Sweden
for the period 1990–2013: one time-consistent reanalysis and one using all
available hourly measurements. Both data sets are available upon request from
the corresponding author.
We evaluated the performance of the reanalyzed near-surface O3 and
mainly found improved performance compared to the MATCH model.
Our results show the following:
High near-surface O3 concentrations in Sweden are decreasing and low
O3 concentrations are increasing.
Health and vegetation impacts due to high near-surface O3
concentrations (quantified by policy-related threshold metrics) have
decreased in central and south Sweden as a result of the decrease in the
highest ozone values.
Decreasing emissions in Europe have led to decreasing summertime
near-surface O3 concentrations, as well as a decrease in the highest
concentrations.
The rising low concentrations of near-surface O3 in Sweden are caused
by a combination of rising hemispheric background O3 concentrations,
meteorological variations and O3 response to European O3 precursor
emission regulation.
There is a discrepancy between modeled and observed (reanalyzed) O3
trends in northern Sweden. This could be caused by erroneous trends in the
historical anthropogenic ozone precursor emissions used here or that our
model is too sensitive to changes in emissions. If the latter is true, it
implies that the evolution of future precursor emissions may have a weaker
impact on future near-surface O3 concentrations than shown in earlier
studies (e.g., Langner et al., 2012a, b; Watson et al., 2016).
The results show that the impact of meteorological variability changes
strongly from lower percentiles levels to the very highest in the south. In
studies of future development, maximum ozone, and what causes it to change, should be investigated further.
The data sets is available upon request from the authors.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-13869-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This project was funded by the Swedish Environmental Protection Agency
(EPA), through funding directly to the reanalysis (contract no. 2251-14-016)
and through the research program Swedish Clean Air and Climate (SCAC) and
NordForsk through the research program Nordic WelfAir (grant no. 75007).
The annual mapping with the MATCH Sweden system is funded by the Swedish
EPA.
Thanks to Sverre Solberg (NILU, Norway) for all of the help, especially with the
selection of Norwegian observation sites.
Edited by: Maria Kanakidou
Reviewed by: two anonymous referees
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