<figure><img src="image_12.png" /><figcaption>**Figure 10: Annual peak ozone values around the world.** (Top) Monthly means of maximum daily 8-hour ozone, corresponding to the peak month of the year. Data are averaged across 2° × 2° grid cells for the period 2005–2014, using all available ozone monitoring sites below 2000 m elevation. (Bottom) The same data, but shown by latitude and colored by month to indicate the time of year that a given grid cell experiences peak ozone levels. Gridded data were produced by *Schultz et al.* [2017] for the Tropospheric Ozone Assessment Report, available at: https://doi.org/10.1594/PANGAEA.876108. DOI: https://doi.org/10.1525/elementa.420.f10</figcaption></figure>

at the surface [Lacis et al., 1990; Forster and Shine, 1997; Worden et al., 2008, 2011; Bowman et al., 2013; Kuai et al., 2017]. Supplemental Table S-3 summarizes recent studies that have quantified trends of free tropospheric ozone and tropospheric column ozone at multiple locations around the world based on ozonesondes and IAGOS commercial aircraft observations [Oltmans et al., 2013; Cooper et al., 2014; Tarasick et al., 2016; Van Malderen et al., 2016; Cohen et al., 2018; Gaudel et al., 2018], and serves as an update to a similar table that appeared in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

[IPCC, 2013; see Table 2.SM.1 in Hartmann et al., 2013]. Focusing on time series at least 20-years in length, Table S-3 demonstrates a general increase of ozone in the free troposphere of both hemispheres, although a few regions show no increase, or even a decrease in a few isolated layers of the troposphere. For the longest time series (>30 years) the trends can vary across shorter time periods, depending on the decades chosen for analysis [Oltmans et al., 2013; Tarasick et al., 2016]. These findings are consistent with the earlier assessment by IPCC [IPCC, 2013]. The ozone increases are especially prevalent across the
northern tropics and mid-latitudes, with the strongest increases above South and East Asia in the range 2–6 ppbv decade⁻¹, which are generally far greater than trends at the remote surface sites (Table 2). Clearly, the highly variable positive and negative trends at the surface, as shown in Figure 5, are not indicative of ozone trends in the free troposphere, which are overwhelmingly positive. This comparison is a further demonstration that scattered surface ozone trends cannot be assumed to be representative of ozone trends in other regions and levels of the troposphere.

While the TOAR database reveals the observation-based surface ozone distribution from all available monitoring sites (see Figure 10, and Gaudel et al. [2018]), very large data gaps exist and models must be used for a complete global picture. The surface ozone distribution from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) multi-model ensemble generally captures the same ozone peaks as the TOAR observations but with a high bias [Young et al., 2013a, 2013b, 2018]. The ACCMIP ensemble also indicates that the northern mid-latitude ozone enhancement (Figure 10) circles the globe, and is present in regions without ozone observations. Comparison to model simulations of pre-industrial ozone indicates that the northern mid-latitude ozone enhancement is due to anthropogenic emissions [Young et al. 2013a, 2013b]. Over time, the region of the Northern Hemisphere with the greatest net ozone production due to anthropogenic emissions has migrated southward as emissions have shifted from North America and Europe to Asia. A model study determined that emissions changes (in terms of magnitude and location) increased lower tropospheric ozone in the 10°–30° N latitude band by 3–5 ppb over the period 1980–2010 [Zhang et al., 2016]. This modeled rate of increase (1.0–1.7 ppbv decade⁻¹) is similar to the observed rates at MLO and Izaña (Table 2). No other long-term remote monitoring sites are available from high elevations in the 10°–30° N latitude band, however additional support is provided by IAGOS commercial aircraft profiles, which revealed lower tropospheric ozone increases above southern India and Southeast Asia from the period 1994–2004 to 2005–2014 [Gaudel et al., 2018]. Furthermore, the Hok Tsui coastal site just south of Hong Kong has measured ozone in this very polluted region of southern China since 1994. The data have been filtered by Wang et al. [2009; 2019] to focus on marine air masses from the South China Sea, revealing a strong ozone increase of 3.6 ppbv decade⁻¹.

Finally, we show how newly available ozone observations (since 2010) have updated our understanding of ozone variability at the three high elevation Alpine sites. Previous analysis of long-term ozone trends at these sites concluded that ozone increased throughout the 1990s, peaked in the early 2000s and then decreased until the time series ended in 2009–2011 [Parrish et al., 2012, 2014]. The rates of decrease were strongest in summer and were determined with quadratic fits. Our analysis, based on the lowess smoother, also shows ozone maxima in the late 1990s or early 2000s, followed by decreasing ozone until approximately 2010 (see time series plots in

Appendix S-B in the Supplemental Material). However, the newly available data through 2016–2018 show that ozone has remained approximately level at the three Alpine sites since 2010, with a slight increase since 2015 at Jungfraujoch. When focusing on just the summer months, ozone in recent years is consistent with the overall decreasing trends observed at these sites since the late 1990s or early 2000s (Section 3.3 and Table S-1). In contrast, ozone in the other seasons has remained level or increased slightly (e.g. Jungfraujoch in winter, Table S-1), resulting in annually averaged ozone levels that have changed little since 2010. Extension of these time series with future observations, and application of the lowess smoother to the new data, can quickly reveal if the near-level ozone values of recent years will continue.

## 5. Conclusions

This study has estimated surface ozone trends at 27 globally distributed remote locations (20 in the Northern Hemisphere, and 7 in the Southern Hemisphere), focusing on continuous ozone time series that extend from the present back to at least 1995. The analysis builds on the original TOAR effort by providing multi-decadal trend analysis at 22 remote sites that were not explored by Gaudel et al. [2018]. To avoid the confounding influence of ozone's seasonal cycle and periods of missing data, the trends are based on monthly mean ozone anomalies rather than ambient ozone mixing ratios, using a statistical method that was not employed by TOAR [Gaudel et al., 2018]. Since 1995, ozone trends at the available Northern Hemisphere sites are nearly evenly split between positive and negative trends, with more than half of the trends being relatively weak, between 1 and −1 ppbv decade⁻¹ (p-values typically greater than 0.10); the Southern Hemisphere had positive trends at 5 out of 7 sites. In both hemispheres, the robust positive trends (p-values less than 0.10) are in the range of 0.5–2 ppbv decade⁻¹. The longest time series available are from South Pole, where ozone is essentially unchanged since the early 1960s, and Mauna Loa, Hawaii where ozone has increased by roughly 50% since the late 1950s (~2.3 ppbv decade⁻¹).

Particular focus was placed on the high elevation Alpine monitoring sites, which were compared to IAGOS commercial aircraft observations in the lower free-troposphere above Western Europe. The Alpine sites are frequently impacted by the polluted European boundary layer, especially in summer, and can only be used to quantify lower free tropospheric ozone if the time series are carefully filtered to avoid observations impacted by the boundary layer. In contrast, the IAGOS observations at 650 hPa are above the European boundary layer, and show that ozone in the lower free troposphere above Western Europe has increased at the rate of 1.3 ± 0.8 ppbv decade⁻¹ since 1994. Stronger increases were detected in the mid- and upper troposphere (1.5–4.5 ppbv decade⁻¹).

We emphasize that while we used all long-term remote monitoring sites available, the data are only representative of much less than 25% of the global surface area, and therefore they cannot be used to produce a global mean surface ozone trend. Full understanding of global surface
northern tropics and mid-latitudes, with the strongest increases above South and East Asia in the range 2–6 ppbv decade⁻¹, which are generally far greater than trends at the remote surface sites (Table 2). Clearly, the highly variable positive and negative trends at the surface, as shown in Figure 5, are not indicative of ozone trends in the free troposphere, which are overwhelmingly positive. This comparison is a further demonstration that scattered surface ozone trends cannot be assumed to be representative of ozone trends in other regions and levels of the troposphere.

While the TOAR database reveals the observation-based surface ozone distribution from all available monitoring sites (see Figure 10, and Gaudel et al. [2018]), very large data gaps exist and models must be used for a complete global picture. The surface ozone distribution from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) multi-model ensemble generally captures the same ozone peaks as the TOAR observations but with a high bias [Young et al., 2013a, 2013b, 2018]. The ACCMIP ensemble also indicates that the northern mid-latitude ozone enhancement (Figure 10) circles the globe, and is present in regions without ozone observations. Comparison to model simulations of pre-industrial ozone indicates that the northern mid-latitude ozone enhancement is due to anthropogenic emissions [Young et al. 2013a, 2013b]. Over time, the region of the Northern Hemisphere with the greatest net ozone production due to anthropogenic emissions has migrated southward as emissions have shifted from North America and Europe to Asia. A model study determined that emissions changes (in terms of magnitude and location) increased lower tropospheric ozone in the 10°–30° N latitude band by 3–5 ppb over the period 1980–2010 [Zhang et al., 2016]. This modeled rate of increase (1.0–1.7 ppbv decade⁻¹) is similar to the observed rates at MLO and Izaña (Table 2). No other long-term remote monitoring sites are available from high elevations in the 10°–30° N latitude band, however additional support is provided by IAGOS commercial aircraft profiles, which revealed lower tropospheric ozone increases above southern India and Southeast Asia from the period 1994–2004 to 2005–2014 [Gaudel et al., 2018]. Furthermore, the Hok Tsui coastal site just south of Hong Kong has measured ozone in this very polluted region of southern China since 1994. The data have been filtered by Wang et al. [2009; 2019] to focus on marine air masses from the South China Sea, revealing a strong ozone increase of 3.6 ppbv decade⁻¹.

Finally, we show how newly available ozone observations (since 2010) have updated our understanding of ozone variability at the three high elevation Alpine sites. Previous analysis of long-term ozone trends at these sites concluded that ozone increased throughout the 1990s, peaked in the early 2000s and then decreased until the time series ended in 2009–2011 [Parrish et al., 2012, 2014]. The rates of decrease were strongest in summer and were determined with quadratic fits. Our analysis, based on the lowess smoother, also shows ozone maxima in the late 1990s or early 2000s, followed by decreasing ozone until approximately 2010 (see time series plots in

Appendix S-B in the Supplemental Material). However, the newly available data through 2016–2018 show that ozone has remained approximately level at the three Alpine sites since 2010, with a slight increase since 2015 at Jungfraujoch. When focusing on just the summer months, ozone in recent years is consistent with the overall decreasing trends observed at these sites since the late 1990s or early 2000s (Section 3.3 and Table S-1). In contrast, ozone in the other seasons has remained level or increased slightly (e.g. Jungfraujoch in winter, Table S-1), resulting in annually averaged ozone levels that have changed little since 2010. Extension of these time series with future observations, and application of the lowess smoother to the new data, can quickly reveal if the near-level ozone values of recent years will continue.

## 5. Conclusions

This study has estimated surface ozone trends at 27 globally distributed remote locations (20 in the Northern Hemisphere, and 7 in the Southern Hemisphere), focusing on continuous ozone time series that extend from the present back to at least 1995. The analysis builds on the original TOAR effort by providing multi-decadal trend analysis at 22 remote sites that were not explored by Gaudel et al. [2018]. To avoid the confounding influence of ozone's seasonal cycle and periods of missing data, the trends are based on monthly mean ozone anomalies rather than ambient ozone mixing ratios, using a statistical method that was not employed by TOAR [Gaudel et al., 2018]. Since 1995, ozone trends at the available Northern Hemisphere sites are nearly evenly split between positive and negative trends, with more than half of the trends being relatively weak, between 1 and −1 ppbv decade⁻¹ (p-values typically greater than 0.10); the Southern Hemisphere had positive trends at 5 out of 7 sites. In both hemispheres, the robust positive trends (p-values less than 0.10) are in the range of 0.5–2 ppbv decade⁻¹. The longest time series available are from South Pole, where ozone is essentially unchanged since the early 1960s, and Mauna Loa, Hawaii where ozone has increased by roughly 50% since the late 1950s (~2.3 ppbv decade⁻¹).

Particular focus was placed on the high elevation Alpine monitoring sites, which were compared to IAGOS commercial aircraft observations in the lower free-troposphere above Western Europe. The Alpine sites are frequently impacted by the polluted European boundary layer, especially in summer, and can only be used to quantify lower free tropospheric ozone if the time series are carefully filtered to avoid observations impacted by the boundary layer. In contrast, the IAGOS observations at 650 hPa are above the European boundary layer, and show that ozone in the lower free troposphere above Western Europe has increased at the rate of 1.3 ± 0.8 ppbv decade⁻¹ since 1994. Stronger increases were detected in the mid- and upper troposphere (1.5–4.5 ppbv decade⁻¹).

We emphasize that while we used all long-term remote monitoring sites available, the data are only representative of much less than 25% of the global surface area, and therefore they cannot be used to produce a global mean surface ozone trend. Full understanding of global surface
ozone trends based on observations would require a major expansion of the surface ozone observation network. Based on newly developed methods [Sofen et al., 2016; Weatherhead et al., 2017; Chang et al., 2019] the locations of these additional sites could be carefully planned to maximize their spatial representativeness.

We conducted a review of the recent peer-reviewed literature to summarize free tropospheric (primarily in the range 700–300 hPa) and tropospheric column ozone trends around the world, beginning in 1995 or earlier (Table S-3). The results revealed a general increase of ozone in the free troposphere of both hemispheres, although a few sites showed no increase, or even a decrease in a few isolated layers of the troposphere, and the trends can vary depending on the time period chosen for analysis. Since the mid-1990s free tropospheric ozone increases are especially prevalent across the northern tropics and mid-latitudes, with the strongest increases above South and East Asia in the range 2–6 ppbv decade⁻¹, which are generally far greater than trends at the remote surface sites, either at sea level or on mountaintops. We conclude that the highly variable positive and negative trends at the surface are not necessarily indicative of ozone trends in the free troposphere, which have been overwhelmingly positive since the mid-1990s.

The ozone time series presented in this analysis provide an important benchmark for the evaluation of multi-decadal chemistry-climate model simulations of tropospheric ozone. We recommend that these remote sites constitute one component of a three-step approach:

1) Because ozone has a greater impact on radiative forcing in the free troposphere than at the surface, the models should be evaluated against in situ free tropospheric observations from ozonesondes and commercial aircraft [Liu et al., 2013; Cohen et al., 2018; Gaudel et al., 2018; Stauffer et al., 2018; Tarasick and Galbally et al., 2019].

2) Evaluating coarse-resolution chemistry climate models in regions of high ozone precursor emissions can be challenging, due to the difference in scale between a model grid cell and ozone observations at a single polluted location. For this purpose TOAR has provided pre-processed gridded products at 2° × 2° and 5° × 5° resolution based on all, rural and urban observations [Schultz et al., 2017; Young et al., 2018].

3) Ozone production and loss proceed at different rates at remote surface sites than in the free troposphere, and the ozone time series presented in this paper are ideal for evaluating model surface processes at remote locations [Young et al., 2018; Lin et al., 2019].

Care is required when selecting the appropriate model grid cell and vertical layer for comparison to these remote surface sites. For example, the Cape Point data were filtered for onshore flow conditions when transport is from the west and south. Therefore to focus on conditions above the South Atlantic Ocean a model grid cell should be chosen that is to the west of Cape Town where recent continental outflow is infrequent. For a mountaintop site, such as Jungfraujoch, the actual elevation of the sta-

tion will likely be far above the coarse model terrain, and therefore a grid cell should be chosen that does not reside in the free troposphere above the Alpine boundary layer. However, this complication does not arise for Mauna Loa because the terrain of Hawaii is not resolved by a coarse-resolution global-scale model. Therefore, a direct comparison can be made between the nighttime ozone observations at Mauna Loa (representative of the lower free troposphere) and a free tropospheric model grid cell at the same elevation. Selection of the most appropriate grid cell for model evaluation will vary by model. We recommend that a range of grid cells surrounding the sampling coordinates be examined, in order to identify the cell that most closely matches the sampling environment of the remote monitoring site.

Once the appropriate model time series has been selected for evaluation against the observed ozone time series, we recommend the following straightforward statistical analysis to determine if the modeled long-term ozone trend, bias and interannual variability are consistent with the observed values.

1) Calculate monthly ozone anomalies for the model time series using the same reference period, and drop any monthly values that do not appear in the observed time series.

2) Compare the slopes of the observed and modelled regression lines using the Student's t-test based on the standard error of regression models, paying special attention to the equality of the fitted variances associated with the slopes, as described in the tutorial by Andrade and Estévez-Pérez [2014]. If nonlinear changes are a dominant feature of the trend, as indicated by the lowess smoother, we recommend the application of piecewise linear regression. However, the time series segments must be long enough to arrive at meaningful parameter estimates. The piecewise linear regression method is described in the Supplemental Material, and a recent example is provided by Banerjee et al. [2020].

3) This same method can be used to quantify the model bias.

4) The standard R-squared metric can be used to quantify the variance in the observations that is explained by the model.

5) There is no ideal metric for comparing two non-linear trends (e.g a comparison of the lowess smoother), however the simple root mean squared error (RMSE) can be used for this purpose, as can similar model skill measures [Krause et al., 2005].

6) Additional guidance can be found in the textbooks by Box et al. [2015], Chandler and Scott [2011] and Judd et al. (2011).

The field of climate change research has developed a range of widely accepted statistical methods for the analysis of trends, extreme events and model evaluation [von Storch and Zwiers, 1999; Zwiers and von Storch, 2004; Hennemuth et al., 2013], as well as common practices for communicating uncertainty in assessment findings [Mastrandrea et al., 2011]. Statistical analysis in the field of atmospheric
ozone trends based on observations would require a major expansion of the surface ozone observation network. Based on newly developed methods [Sofen et al., 2016; Weatherhead et al., 2017; Chang et al., 2019] the locations of these additional sites could be carefully planned to maximize their spatial representativeness.

We conducted a review of the recent peer-reviewed literature to summarize free tropospheric (primarily in the range 700–300 hPa) and tropospheric column ozone trends around the world, beginning in 1995 or earlier (Table S-3). The results revealed a general increase of ozone in the free troposphere of both hemispheres, although a few sites showed no increase, or even a decrease in a few isolated layers of the troposphere, and the trends can vary depending on the time period chosen for analysis. Since the mid-1990s free tropospheric ozone increases are especially prevalent across the northern tropics and mid-latitudes, with the strongest increases above South and East Asia in the range 2–6 ppbv decade⁻¹, which are generally far greater than trends at the remote surface sites, either at sea level or on mountaintops. We conclude that the highly variable positive and negative trends at the surface are not necessarily indicative of ozone trends in the free troposphere, which have been overwhelmingly positive since the mid-1990s.

The ozone time series presented in this analysis provide an important benchmark for the evaluation of multi-decadal chemistry-climate model simulations of tropospheric ozone. We recommend that these remote sites constitute one component of a three-step approach:

1) Because ozone has a greater impact on radiative forcing in the free troposphere than at the surface, the models should be evaluated against in situ free tropospheric observations from ozonesondes and commercial aircraft [Liu et al., 2013; Cohen et al., 2018; Gaudel et al., 2018; Stauffer et al., 2018; Tarasick and Galbally et al., 2019].

2) Evaluating coarse-resolution chemistry climate models in regions of high ozone precursor emissions can be challenging, due to the difference in scale between a model grid cell and ozone observations at a single polluted location. For this purpose TOAR has provided pre-processed gridded products at 2° × 2° and 5° × 5° resolution based on all, rural and urban observations [Schultz et al., 2017; Young et al., 2018].

3) Ozone production and loss proceed at different rates at remote surface sites than in the free troposphere, and the ozone time series presented in this paper are ideal for evaluating model surface processes at remote locations [Young et al., 2018; Lin et al., 2019].

Care is required when selecting the appropriate model grid cell and vertical layer for comparison to these remote surface sites. For example, the Cape Point data were filtered for onshore flow conditions when transport is from the west and south. Therefore to focus on conditions above the South Atlantic Ocean a model grid cell should be chosen that is to the west of Cape Town where recent continental outflow is infrequent. For a mountaintop site, such as Jungfraujoch, the actual elevation of the sta-

tion will likely be far above the coarse model terrain, and therefore a grid cell should be chosen that does not reside in the free troposphere above the Alpine boundary layer. However, this complication does not arise for Mauna Loa because the terrain of Hawaii is not resolved by a coarse-resolution global-scale model. Therefore, a direct comparison can be made between the nighttime ozone observations at Mauna Loa (representative of the lower free troposphere) and a free tropospheric model grid cell at the same elevation. Selection of the most appropriate grid cell for model evaluation will vary by model. We recommend that a range of grid cells surrounding the sampling coordinates be examined, in order to identify the cell that most closely matches the sampling environment of the remote monitoring site.

Once the appropriate model time series has been selected for evaluation against the observed ozone time series, we recommend the following straightforward statistical analysis to determine if the modeled long-term ozone trend, bias and interannual variability are consistent with the observed values.

1) Calculate monthly ozone anomalies for the model time series using the same reference period, and drop any monthly values that do not appear in the observed time series.

2) Compare the slopes of the observed and modelled regression lines using the Student's t-test based on the standard error of regression models, paying special attention to the equality of the fitted variances associated with the slopes, as described in the tutorial by Andrade and Estévez-Pérez [2014]. If nonlinear changes are a dominant feature of the trend, as indicated by the lowess smoother, we recommend the application of piecewise linear regression. However, the time series segments must be long enough to arrive at meaningful parameter estimates. The piecewise linear regression method is described in the Supplemental Material, and a recent example is provided by Banerjee et al. [2020].

3) This same method can be used to quantify the model bias.

4) The standard R-squared metric can be used to quantify the variance in the observations that is explained by the model.

5) There is no ideal metric for comparing two non-linear trends (e.g a comparison of the lowess smoother), however the simple root mean squared error (RMSE) can be used for this purpose, as can similar model skill measures [Krause et al., 2005].

6) Additional guidance can be found in the textbooks by Box et al. [2015], Chandler and Scott [2011] and Judd et al. (2011).

The field of climate change research has developed a range of widely accepted statistical methods for the analysis of trends, extreme events and model evaluation [von Storch and Zwiers, 1999; Zwiers and von Storch, 2004; Hennemuth et al., 2013], as well as common practices for communicating uncertainty in assessment findings [Mastrandrea et al., 2011]. Statistical analysis in the field of atmospheric
chemistry has not yet reached this level of maturity. Development of a suite of best statistical practices for atmospheric chemistry would be of great value to the field, and is an endeavor that could be effectively addressed by the next phase of the Tropospheric Ozone Assessment Report (https://igacproject.org/activities/TOAR/TOAR-II).

## Data accessibility statement

The surface ozone observations reported in this paper, from 1970 to the present, can be downloaded from the TOAR Surface Ozone Database via the JOIN web interface: https://join.fz-juelich.de. The historical ozone observations from MLO (1957–1959) and SPO (1961–1963) are available from NOAA's Global Monitoring Laboratory: ftp://aftp.cmdl.noaa.gov/data/ozwv/SurfaceOzone/Historical/. IAGOS aircraft observations are available from: https://doi.org/10.25326/20.

In addition, the monthly surface ozone values upon which the trends were calculated can be easily downloaded as ASCII text files from the following URL: http://doi.org/10.34730/e792cad833174ebcafd9f052711e5660.

## Supplemental file

The supplemental file for this article can be found as follows:

* **Supplemental Material.** Supplemental figures, tables and appendices. DOI: https://doi.org/10.1525/elementa.420.s1

## Acknowledgements

We thank Forschungszentrum Jülich for funding of the TOAR database development and its maintenance. Many of the times series in this analysis were obtained from the EBAS database, which is largely funded by the UN-ECE CLRTAP (EMEP), AMAP and through Norwegian Institute for Air Research (NILU) internal resources. Specific developments have been possible due to projects like EUSAAR (EU-FP5), EBAS-Online (Norwegian Research Council INFRA) and HTAP (European Commission DG-ENV). A large number of specific projects have supported development of data and meta data reporting schemes in dialog with EBAS data providers. The IAGOS program acknowledges the European Commission for its support of the MOZAIC project (1994–2003) the preparatory phase of IAGOS (2005–2013) and IGAS (2013–2016); the partner institutions of the IAGOS Research Infrastructure (FZJ, DLR, MPI, KIT in Germany, CNRS, Météo-France, Université Paul Sabatier in France, and University of Manchester, UK); the French Atmospheric Data Center AERIS for hosting the database; and the participating airlines (Lufthansa, Air France, China Airlines, Iberia, Cathay Pacific, Hawaiian Airlines) for transporting the instrumentation free of charge. Special thanks also goes to the following scientists, air quality specialists and agencies for providing ozone data from the remote and rural monitoring sites: Weili Lin, Meteorological Observation Center, China Meteorological Administration, Beijing, for the Mt. Waliguan data; Rolf Weller, Alfred Wegener Institute, Germany, for the Neumayer, Antarctica data; Mr. Kazuyuki Saito (ghgobs@met.kishou.go.jp), Japan Meteorological

Agency (JMA) for the Minamitorishima GAW global station data; Gary Lear, U.S. Environmental Protection Agency Clean Air Markets Division Clean Air Status and Trends Network (CASTNET), for the data from Denali, Gothic, Great Basin, Grand Canyon and Centennial; Ute Dauert and Stefan Feigenspahn, Umweltbundesamt Dessau, Germany for obtaining the ozone measurements from the German federal air quality network; Thumeka Mkololo, South African Weather Service, for providing the baseline filtered ozone data at Cape Point; Gastón Torres Aravena, Dirección Meteorológica de Chile, for providing the data from El Tololo, Chile; Iris Buxbaum, Umweltbundesamt/Federal Environment Agency, Austria, for providing the Sonnblick data; Landesamt für Umwelt, Wasserwirtschaft und Gewerbeaufsicht, Rheinland Pfalz provided the Pfälzerwald-Hortenkopf measurements; Landesanstalt für Umwelt, Messungen und Naturschutz, Baden-Württemberg provided the Schwarzwald-Süd measurements; the Scottish Environment Protection Agency provided the Strath Vaich Dam data. Martin Steinbacher acknowledges funding from the GAW Quality Assurance/Science Activity Centre Switzerland (QA/SAC-CH), which is supported by MeteoSwiss and Empa.

## Competing interests

The authors have no competing interests to declare.

## Author contributions

* **Contributed to conception and design:** ORC, MGS, KLC

* **Contributed to acquisition of data:** MGS, SSchröder, GCB, EC, MF, IEG, DK, XL, AMB, SM, PN, JOB, IP, LR, IS, KS, SSolberg, TGS, WS, MS, DT, VT, XX, IB

* **Contributed to analysis and interpretation of data:** ORC, MGS, SSchröder, KLC, AG, IEG, XL, MS, DT

* **Drafted and/or revised the article:** ORC, MGS, SSchröder, KLC, EC, IEG, JOB, IP, SSolberg, MS, DT, VT, XX

* **Approved the submitted version for publication:** All authors

## References

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Anet, JG, Steinbacher, M, Gallardo, L, Velásquez Álvarez, PA, Emmenegger, L and Buchmann, B. 2017. Surface ozone in the Southern Hemisphere: 20 years of data from a site with a unique setting in El Tololo, Chile. *Atmos. Chem. Phys.*, **17**: 6477–6492. DOI: https://doi.org/10.5194/acp-17-6477-2017

Baltensperger, U, Gäggeler, HW, Jost, DT, Lugauer, M, Schwikowski, M, Weingartner, E and Seibert, P. 1997. Aerosol climatology at the high-alpine site Jungfraujoch, Switzerland. *J. Geophys. Res.*, **102**(D16): 19707–19715. DOI: https://doi.org/10.1029/97JD00928

Banerjee, A, Fyfe, JC, Polvani, LM, Waugh, D and Chang, K-L. 2020. A pause in Southern Hemisphere
chemistry has not yet reached this level of maturity. Development of a suite of best statistical practices for atmospheric chemistry would be of great value to the field, and is an endeavor that could be effectively addressed by the next phase of the Tropospheric Ozone Assessment Report (https://igacproject.org/activities/TOAR/TOAR-II).

## Data accessibility statement

The surface ozone observations reported in this paper, from 1970 to the present, can be downloaded from the TOAR Surface Ozone Database via the JOIN web interface: https://join.fz-juelich.de. The historical ozone observations from MLO (1957–1959) and SPO (1961–1963) are available from NOAA's Global Monitoring Laboratory: ftp://aftp.cmdl.noaa.gov/data/ozwv/SurfaceOzone/Historical/. IAGOS aircraft observations are available from: https://doi.org/10.25326/20.

In addition, the monthly surface ozone values upon which the trends were calculated can be easily downloaded as ASCII text files from the following URL: http://doi.org/10.34730/e792cad833174ebcafd9f052711e5660.

## Supplemental file

The supplemental file for this article can be found as follows:

* **Supplemental Material.** Supplemental figures, tables and appendices. DOI: https://doi.org/10.1525/elementa.420.s1

## Acknowledgements

We thank Forschungszentrum Jülich for funding of the TOAR database development and its maintenance. Many of the times series in this analysis were obtained from the EBAS database, which is largely funded by the UN-ECE CLRTAP (EMEP), AMAP and through Norwegian Institute for Air Research (NILU) internal resources. Specific developments have been possible due to projects like EUSAAR (EU-FP5), EBAS-Online (Norwegian Research Council INFRA) and HTAP (European Commission DG-ENV). A large number of specific projects have supported development of data and meta data reporting schemes in dialog with EBAS data providers. The IAGOS program acknowledges the European Commission for its support of the MOZAIC project (1994–2003) the preparatory phase of IAGOS (2005–2013) and IGAS (2013–2016); the partner institutions of the IAGOS Research Infrastructure (FZJ, DLR, MPI, KIT in Germany, CNRS, Météo-France, Université Paul Sabatier in France, and University of Manchester, UK); the French Atmospheric Data Center AERIS for hosting the database; and the participating airlines (Lufthansa, Air France, China Airlines, Iberia, Cathay Pacific, Hawaiian Airlines) for transporting the instrumentation free of charge. Special thanks also goes to the following scientists, air quality specialists and agencies for providing ozone data from the remote and rural monitoring sites: Weili Lin, Meteorological Observation Center, China Meteorological Administration, Beijing, for the Mt. Waliguan data; Rolf Weller, Alfred Wegener Institute, Germany, for the Neumayer, Antarctica data; Mr. Kazuyuki Saito (ghgobs@met.kishou.go.jp), Japan Meteorological

Agency (JMA) for the Minamitorishima GAW global station data; Gary Lear, U.S. Environmental Protection Agency Clean Air Markets Division Clean Air Status and Trends Network (CASTNET), for the data from Denali, Gothic, Great Basin, Grand Canyon and Centennial; Ute Dauert and Stefan Feigenspahn, Umweltbundesamt Dessau, Germany for obtaining the ozone measurements from the German federal air quality network; Thumeka Mkololo, South African Weather Service, for providing the baseline filtered ozone data at Cape Point; Gastón Torres Aravena, Dirección Meteorológica de Chile, for providing the data from El Tololo, Chile; Iris Buxbaum, Umweltbundesamt/Federal Environment Agency, Austria, for providing the Sonnblick data; Landesamt für Umwelt, Wasserwirtschaft und Gewerbeaufsicht, Rheinland Pfalz provided the Pfälzerwald-Hortenkopf measurements; Landesanstalt für Umwelt, Messungen und Naturschutz, Baden-Württemberg provided the Schwarzwald-Süd measurements; the Scottish Environment Protection Agency provided the Strath Vaich Dam data. Martin Steinbacher acknowledges funding from the GAW Quality Assurance/Science Activity Centre Switzerland (QA/SAC-CH), which is supported by MeteoSwiss and Empa.

## Competing interests

The authors have no competing interests to declare.

## Author contributions

* **Contributed to conception and design:** ORC, MGS, KLC

* **Contributed to acquisition of data:** MGS, SSchröder, GCB, EC, MF, IEG, DK, XL, AMB, SM, PN, JOB, IP, LR, IS, KS, SSolberg, TGS, WS, MS, DT, VT, XX, IB

* **Contributed to analysis and interpretation of data:** ORC, MGS, SSchröder, KLC, AG, IEG, XL, MS, DT

* **Drafted and/or revised the article:** ORC, MGS, SSchröder, KLC, EC, IEG, JOB, IP, SSolberg, MS, DT, VT, XX

* **Approved the submitted version for publication:** All authors

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Derwent, RG, Manning, AJ, Simmonds, PG, Spain, TG and O'Doherty, S. 2018. Long-term trends in ozone in baseline and European regionally-polluted air at Mace Head, Ireland over a 30-year period. *Atmos. Environ.*, **179**: 279–287. DOI: https://doi.org/10.1016/j.atmosenv.2018.02.024

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**How to cite this article:** Cooper, OR, Schultz, MG, Schröder, S, Chang, K-L, Gaudel, A, Benítez, GC, Cuevas, E, Fröhlich, M, Galbally, IE, Molloy, S, Kubistin, D, Lu, X, McClure-Begley, A, Nédélec, P, O'Brien, J, Oltmans, SJ, Petropavlovskikh, I, Ries, L, Senik, I, Sjöberg, K, Solberg, S, Spain, GT, Spangl, W, Steinbacher, M, Tarasick, D, Thouret, V and Xu, X. 2020. Multi-decadal surface ozone trends at globally distributed remote locations. *Elem Sci Anth*, **8**: 23. DOI: https://doi.org/10.1525/elementa.420

**Domain Editor-in-Chief:** Detlev Helmig, Institute of Alpine and Arctic Research, University of Colorado Boulder, US

**Associate Editor:** Ian Faloona, Land, Air & Water Resources, University of California Davis, US

**Knowledge Domain:** Atmospheric Science

**Submitted:** 13 August 2019

**Accepted:** 07 May 2020

**Published:** 08 June 2020

**Copyright:** © 2019 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
and CarbonTracker Model Over the Zugspitze Region. *Remote Sensing* 11(24): 2981. DOI: https://doi.org/10.3390/rs11242981

Zanis, P, Schuepbach, E, Scheel, H, Baudenbacher, M and Buchmann, B. 1999. Inhomogeneity and trends in the surface ozone record (1988–1996) at Jungfraujoch in the Swiss Alps. *Atmos. Environ.*, **33**: 3777–3786. DOI: https://doi.org/10.1016/S1352-2310(99)00088-6

Zellweger, C, Forrer, J, Hofer, P, Nyeki, S, Schwarzenbach, B, Weingartner, E, Ammann, M and Baltensperger, U. 2003. Partitioning of reactive nitrogen (NO<sub>y</sub>) and dependence on meteorological conditions in the lower free troposphere. *Atmospheric Chemistry and Physics* **3**(3): 779–796. DOI: https://doi.org/10.5194/acp-3-779-2003

Zellweger, C, Hüglin, C, Klausen, J, Steinbacher, M, Vollmer, M and Buchmann, B. 2009. Intercomparison of four different carbon monoxide measurement techniques and evaluation of the long-term carbon monoxide time series of Jungfraujoch. *Atmospheric Chemistry and Physics* **9**(11): 3491–3503. DOI: https://doi.org/10.5194/acp-9-3491-2009

Zellweger, C, Steinbacher, M and Buchmann, B. 2011. System and performance audit of surface ozone, methane, carbon dioxide, nitrous oxide and carbon

monoxide at the Global GAW Station Zugspitze-Schneefernerhaus, Germany, June 2011, WCC-Empa Report 11/2. https://www.empa.ch/web/s503/wcc-empa.

Zhang, Y, Cooper, OR, Gaudel, A, Thompson, AM, Nédélec, P, Ogino, S-Y and West, JJ. 2016. Tropospheric ozone change from 1980 to 2010 dominated by equatorward redistribution of emissions. *Nature Geoscience* **9**(12): 875. DOI: https://doi.org/10.1038/ngeo2827

Ziemke, JR, Oman, LD, Strode, SA, Douglass, AR, Olsen, MA, McPeters, RD, Bhartia, PK, Froidevaux, L, Labow, GJ, Witte, JC, Thompson, AM, Haffner, DP, Kramarova, NA, Frith, SM, Huang, L-K, Jaross, GR, Seftor, CJ, Deland, MT and Taylor, SL. 2019. Trends in global tropospheric ozone inferred from a composite record of TOMS/OMI/MLS/OMPS satellite measurements and the MERRA-2 GMI simulation. *Atmos. Chem. Phys.*, **19**: 3257–3269. DOI: https://doi.org/10.5194/acp-19-3257-2019

Zwiers, FW and Von Storch, H. 2004. On the role of statistics in climate research. *International Journal of Climatology: A Journal of the Royal Meteorological Society* **24**(6): 665–680. DOI: https://doi.org/10.1002/joc.1027

**How to cite this article:** Cooper, OR, Schultz, MG, Schröder, S, Chang, K-L, Gaudel, A, Benítez, GC, Cuevas, E, Fröhlich, M, Galbally, IE, Molloy, S, Kubistin, D, Lu, X, McClure-Begley, A, Nédélec, P, O'Brien, J, Oltmans, SJ, Petropavlovskikh, I, Ries, L, Senik, I, Sjöberg, K, Solberg, S, Spain, GT, Spangl, W, Steinbacher, M, Tarasick, D, Thouret, V and Xu, X. 2020. Multi-decadal surface ozone trends at globally distributed remote locations. *Elem Sci Anth*, **8**: 23. DOI: https://doi.org/10.1525/elementa.420

**Domain Editor-in-Chief:** Detlev Helmig, Institute of Alpine and Arctic Research, University of Colorado Boulder, US

**Associate Editor:** Ian Faloona, Land, Air & Water Resources, University of California Davis, US

**Knowledge Domain:** Atmospheric Science

**Submitted:** 13 August 2019

**Accepted:** 07 May 2020

**Published:** 08 June 2020

**Copyright:** © 2019 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.