# Observational ozone datasets over the global oceans and polar regions (version 2024)

Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K Koenig, Kaori Kawana, James E Johnson, Aurélie Colomb, Pierre Tulet, Suzie Molloy, et al.

► To cite this version:

Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K Koenig, et al. Observational ozone datasets over the global oceans and polar regions (version 2024). Earth System Science Data, 2025, 17 (9), pp.4901-4932. 10.17596/0004044. hal-05294275

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Observational ozone datasets over the global oceans and polar regions (version 2024)

Yugo Kanaya¹, Roberto Sommariva²,³, Alfonso Saiz-Lopez⁴, Andrea Mazzeo⁵, Theodore K. Koenig⁶,⁷, Kaori Kawana¹,⁴,⁷,⁸, James E. Johnson⁷,⁸, Aurélie Colomb⁹, Pierre Tulet¹⁰, Suzie Molloy¹¹,

Ian E. Galbally¹², Rainer Volkamer¹³,¹⁴, Anoop Mahajan¹⁵, John W. Halfacre¹⁶, Paul B. Shepson¹⁷,
Julia Schmale¹⁸, Hélène Angot¹⁹, Byron Blomquist¹⁴, Matthew D. Shupe²⁰,¹⁴, Detlev Helmig²¹,
Junsu Gil²²,⁴⁶, Meehye Lee²², Sean C. Coburn²³, Ivan Ortega²⁴, Gao Chen²⁵, James Lee²⁶,²⁷,
Kenneth C. Aikin¹⁴,²⁸, David D. Parrish²⁹, John S. Holloway¹⁴, Thomas B. Ryerson²⁸,

Ilana B. Pollack¹⁴,²⁸, Eric J. Williams¹⁴,²⁸, Brian M. Lerner³⁰, Andrew J. Weinheimer²⁴,
Teresa Campos²⁴, Frank M. Flocke²⁴, J. Ryan Spackman³¹, Ilann Bourgeois³², Jeff Peischl¹⁴,²⁸,
Chelsea R. Thompson²⁸, Ralf M. Staebler³³, Amir A. Aliabadi³⁴, Wanmin Gong³³,

Roeland Van Malderen³⁵, Anne M. Thompson³⁶, Ryan M. Stauffer³⁶, Debra E. Kollonige³⁶,

Juan Carlos Gómez Martín³⁷, Masatomo Fujiwara³⁸, Katie Read²⁷, Matthew Rowlinson²⁷,¹⁶,

Keiichi Sato³⁹, Junichi Kurokawa³⁹, Yoko Iwamoto⁴⁰, Fumikazu Taketani¹, Hisahiro Takashima⁴¹,¹,
Mónica Navarro-Comas⁴², Marios Panagi⁴³, and Martin G. Schultz⁴⁴,⁴⁵

¹Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Kanagawa, Japan

²School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

³School of Chemistry, University of Leicester, Leicester, UK

⁴Department of Atmospheric Chemistry and Climate, Institute of Physical Chemistry Blas Cabrera,
CSIC, Madrid, Spain

⁵Lancaster Environment Centre, Lancaster University, Lancaster, UK

⁶State Key Joint Laboratory of Environmental Simulation and Pollution Control, BIC-ESAT and IJRC,
College of Environmental Sciences and Engineering, Peking University, Beijing, China

⁷Cooperative Institute for Climate, Ocean, and Ecosystem Studies,
University of Washington, Seattle, WA, USA

⁸NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA

⁹LAMP, Laboratoire de Météorologie Physique, UMR 6016, Université Clermont Auvergne,
CNRS, Aubière, France

¹⁰LAERO, Laboratoire d'Aérologie, UMR 5560 CNRS, UT3, IRD, Toulouse, France

¹¹Climate, Atmosphere & Ocean Interactions program, Environment Research Unit,
CSIRO, Aspendale, Victoria, Australia

¹²CSIRO Environment, Aspendale, Victoria, Australia

¹³Department of Chemistry, University of Colorado Boulder, Boulder, CO, USA

¹⁴Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado Boulder, Boulder, CO, USA

¹⁵Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pashan, Pune, India

¹⁶Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry,
University of York, York, UK

¹⁷School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA

¹⁸Extreme Environments Research Laboratory, École Polytechnique Fédérale de Lausanne,
EPFL Valais Wallis, Sion, Switzerland

¹⁹Université Grenoble Alpes, CNRS, INRAE, IRD, Grenoble INP, IGE, Grenoble, France

²⁰National Oceanic and Atmospheric Administration, Physical Sciences Laboratory, Boulder, CO, USA

²¹Boulder A.I.R. LLC, Boulder, CO, USA
<sup>22</sup>Department of Earth and Environmental Sciences, Korea University, Seoul, Republic of Korea

<sup>23</sup>Precision Laser Diagnostics Laboratory, University of Colorado Boulder, Boulder, CO, USA

<sup>24</sup>NSF National Center for Atmospheric Research, ACOM, Boulder, CO, USA

<sup>25</sup>NASA Langley Research Center, Hampton, VA, USA

<sup>26</sup>Department of Chemistry, University of York, York, UK

<sup>27</sup>National Centre for Atmospheric Science, University of York, York, UK

<sup>28</sup>NOAA Chemical Sciences Laboratory, Boulder, CO, USA

<sup>29</sup>David D. Parrish, LLC, Boulder, CO, USA

<sup>30</sup>Aerodyne Research, Inc., Billerica, MA, USA

<sup>31</sup>NASA Ames Research Center, Moffett Field, CA, USA

<sup>32</sup>Université Savoie Mont Blanc, INRAE, CARRTEL, Thonon-les-Bains, France

<sup>33</sup>Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, ON, Canada

<sup>34</sup>Department of Mechanical Engineering, College of Engineering, University of Guelph, Guelph, ON, Canada

<sup>35</sup>Royal Meteorological Institute of Belgium, Uccle (Brussels), Belgium

<sup>36</sup>NASA Goddard Space Flight Center, Greenbelt, MD, USA

<sup>37</sup>Instituto de Astrofísica de Andalucía, Consejo Superior de Investigaciones Científicas, Granada, Spain

<sup>38</sup>Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan

<sup>39</sup>Asia Center for Air Pollution Research, Niigata, Niigata, Japan

<sup>40</sup>Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan

<sup>41</sup>Faculty of Science, Fukuoka University, Fukuoka, Japan

<sup>42</sup>Atmospheric Research and Instrumentation Branch, National Institute for Aerospace Technology (INTA), Madrid, Spain

<sup>43</sup>Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus

<sup>44</sup>Jülich Supercomputing Center, Forschungszentrum Jülich GmbH, Jülich, Germany

<sup>45</sup>Department of Mathematics and Computer Science, University of Cologne, Cologne, Germany

<sup>46</sup>Institute of Basic Sciences, Korea University, Seoul, Republic of Korea

<sup>a</sup>now at: Institute of Chemical Engineering Sciences, Foundation for Research and Technology – Hellas (FORTH/ICE-HT), Patras, Greece

<sup>b</sup>now at: School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

<sup>c</sup>now at: Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong SAR, China

**Correspondence:** Yugo Kanaya (yugo@jamstec.go.jp)

Received: 28 November 2024 – Discussion started: 13 February 2025

Revised: 24 May 2025 – Accepted: 28 May 2025 – Published: 26 September 2025

**Abstract.** Studying tropospheric ozone over the remote areas of the planet, such as the open oceans and the polar regions, is crucial to understand the role of ozone as a global climate forcer and regulator of atmospheric oxidative capacity. A focus on the pristine oceanic and polar regions complements the available land-based datasets and provides insights into key photochemical and depositional loss processes that control the concentrations and spatiotemporal variability in ozone as well as the physicochemical mechanisms driving these patterns. However, an assessment of the role of ozone over the oceanic and polar regions has been hampered by a lack of comprehensive observational datasets. Here, we present the first comprehensive collection of ozone data over the oceans and the polar regions. The overall dataset consists of 77 ship cruises/buoy-based observations and 48 aircraft-based campaigns. The dataset, consisting of more than 630 000 independent ozone measurement data points covering the period from 1977 to 2022 and an altitude range from the surface to 5000 m (with a focus on the lowest 2000 m), allows systematic analyses of the spatiotemporal distribution and long-term trends over the 11 defined ocean/polar regions. The datasets from ships, buoys, and aircraft are complemented by ozonesonde data from 29 launch sites or field campaigns and by 21 non-polar and 17 polar ground-based station datasets. The datasets contain information on how long the observed air masses were isolated from land, as estimated by backward trajectories from the individual observation points. To extract observations representative of oceanic conditions, we recommend using a subset of the data with an isolation time of 72 h or longer, from the analysis with coincident radon observations. These filtered oceanic and polar data showed typically flat diurnal cycles at high
latitudes, whereas daytime decreases in ozone (11%–16%) were observed at lower latitudes. The ship/buoy- and aircraft-based datasets presented here will supplement the land-based ones in the TOAR-II (Tropospheric Ozone Assessment Report Phase II) database to provide a fully global assessment of tropospheric ozone. The described dataset is available at https://doi.org/10.17596/0004044 (Kanaya et al., 2025).

# 1 Introduction

As a short-lived species with an estimated lifetime of 25.5 ± 2.2 d (Griffiths et al., 2021; Szopa et al., 2021), both global/hemispheric and regional/local aspects need to be emphasized in the assessment of tropospheric ozone. While the spatiotemporal variation over land is primarily important for assessing vegetation and health impacts, its behavior over the oceans is critical when assessing its climate impact as the third most important greenhouse gas (Forster et al., 2021). The role of ozone in maintaining the global oxidative capacity of the atmosphere through the production of the OH radical also requires understanding on a global scale. The overall budget of tropospheric ozone is dominated by the photochemical production and loss terms, estimated at 4500–5000 and 3900–4500 Tg yr⁻¹, respectively, rather than by the stratosphere–troposphere exchange (270–540 Tg yr⁻¹) or surface deposition (800–1000 Tg yr⁻¹) for decades around 2000 or 2010 (Griffiths et al., 2021; Young et al., 2018). The net ozone production mainly occurs over regions with NOₓ pollution and depends on the abundance of volatile organic compounds (VOCs). By contrast, the net loss conditions, which are driven by OH/HO₂ radical chemistry in a low-NOₓ environment and potentially also by understudied halogen chemistry, occur mostly over remote regions, including over the oceans (Galbally et al., 2000; Monks et al., 1998; Stone et al., 2018; Read et al., 2008; Dickerson et al., 1999; Boylan et al., 2015; Saiz-Lopez and von Glasow, 2012; Simpson et al., 2015). Another important loss term is from dry deposition on the ocean surface, which depends on the chemical composition of the surface seawater and its physical conditions (Helmig et al., 2012; Hardacre et al., 2015; Ganzeveld et al., 2009; Pound et al., 2020; Sarwar et al., 2016; Luhar et al., 2018; Barten et al., 2023; Chiu et al., 2024) and which has not been fully characterized. Therefore, there is a special need to study the ozone concentration levels and spatiotemporal variations as well as the underlying mechanisms that control ozone levels over the oceans. However, observational data of ozone over oceanic regions are much less abundant than those over the land, preventing a full assessment. TOAR (Tropospheric Ozone Assessment Report) is an IGAC (International Global Atmospheric Chemistry project)-sponsored activity which aims to collect ozone observations in the troposphere. At the time of TOAR-I (Schultz et al., 2017), oceanic data were collected only from island-based surface monitoring observations and ozoneson-

des, with some satellite-based information, but the oceanic regions remained devoid of data.

The polar regions are other pristine areas, with episodic ozone destruction in the polar sunrise season (Simpson et al., 2007). During the International Polar Year 2007–2008, extensive studies on ozone were conducted and published and published in the POLARCAT (Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate, Chemistry, Aerosols, and Transport) special issue (ACP, 2015). An assessment report on short-lived climate forcers from the Arctic Council’s Arctic Monitoring and Assessment Programme (AMAP, 2021) and synthesis papers (Whaley et al., 2023; Law et al., 2023) have recently been produced, but comprehensive studies based on multi-platform observations are still lacking.

To improve the situation, a working group on ozone over the oceans and the polar regions (Oceans working group) has been formed within the TOAR-II activity to provide a comprehensive dataset, especially with ship-, buoy-, and aircraft-based observations over the oceans, to allow a first assessment over the entire ocean area and polar regions. In the past, studies on ozone over the oceans were conducted using ship-based/aircraft-based ozone observations, but they were mostly based on observations from a single campaign/series of related campaigns (e.g., Dickerson et al., 1999; Bourgeois et al., 2020) or, at best, from a collection of observations from a single nation/project (e.g., Lelieveld et al., 2004; Kanaya et al., 2019); here we aim to collect data from multiple nations, research groups, and campaigns to achieve a better global coverage, using the IGAC framework as a global research network (GRN) of Future Earth, on which the TOAR activity is based. The resulting five datasets include observations from ships/buoys, aircraft, ozonesondes, ground-based coastal/island sites, and polar stations. This data paper primarily describes two of these datasets, which consist of 77 ship- or buoy-based observations and 48 aircraft observations over the global oceans and polar regions covering a period between 1977 and 2022. The altitude range covered by the data is from the surface to 5000 m, with a focus on the lowest 2000 m, as a major interest of the working group lies within the atmospheric boundary layer, where the interfacial interactions of the atmosphere with the ocean and snow/ice surfaces (even including biogeochemical processes), as well as the photochemistry, are of particular relevance. A third dataset contains ocean and polar ozonesonde data, mostly obtained from coastal/island sonde launching sites. The ozonesonde data were mainly
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Locations of ozonesonde and coastal/polar ground observations (a). Overall ship/buoy ozone data (b) and airborne ozone data with altitudes < 2000 m (c) after filtering for LCL ≥ 72 h. Ozone levels above 60 ppb are omitted for clarity.</figcaption></figure>

from Malaspina, SAGA3, DWD-MPI, YES-AQ, and MOSAiC were used as they were provided. We assumed that basic quality control had been performed (e.g., see Angot et al., 2022, for the MOSAiC dataset). The DWD-MPI data are a large compilation of 51 cruises with different research vessels (Meteor, Polarstern, Walther Herwig, Anton Dohrn, Ymer, and Academie Fedorov) collected by Deutscher Wetterdienst (DWD) in the 1977–1996 period, 27 cruises with the container ship *Berlin Express* collected by Max Planck Institute (MPI) in the 1995–2002 period, and one Meteor

cruise conducted by MPI in 2002, with a clear note that the data have been screened for local influences of the research vessel itself and of nearby passing ships and that data in and near harbors and in channels have been removed.

Some cruises included additional observations of pollution tracers, i.e., CO, NO, NO₂, and condensation nuclei (CN) with diameters larger than 11–13 nm, and these data are archived together with ozone (see Table 1 and Table S1 in the Supplement for the observation methods and uncertainties). However, the tracer observations did not cover the entire
dataset and, therefore, could not be used uniformly for further systematic screening of air masses influenced by pollution arising from nearby land (even if present). It is an essential requirement of oceanic ozone studies to be able to distinguish between air masses representing the remote ocean and those influenced by pollution from nearby land. Therefore, we calculated backward trajectories for each hourly dataset to add information on the “last contact with land” (LCL, in the unit of hours ago), as a semi-quantitative index indicating how long the air masses were isolated from a land region with potential pollution before the observations. The land mask data from NASA (NASA, 2019) with a resolution of 0.25° were used. The high-latitude regions (65–90° N or 90–60° S) were not considered to be “land”, and the land mask was assumed only up to an altitude of 2500 m, as the pollution effect from land would be minimal at higher altitudes. Exceptions may include lifted plumes, such as those from biomass burning. The 2500 m threshold is tentative in an attempt to yield a criterion that is meaningful for the entire globe. It may be revisited after detailed analysis in the assessment. Backward trajectories were computed with the HYSPLIT version 4 model (Draxler and Rolph, 2013) using 6-hourly GDAS1 (1 × 1°) meteorological fields after December 2004 or using 6-hourly NCEP/NCAR Reanalysis product (RP{YEAR}{MONTH}.bgl) files with a 2.5° resolution for the earlier period. Harris et al. (2005) studied the uncertainty in the trajectories using the NCEP/NCAR Reanalysis product. The starting altitude for ship/buoy observations was set to 500 m and the duration to 120 h. The first point of land contact was marked to provide the LCL information. An LCL value of 120 h was assigned to the air masses if no land contact occurred for the entire period.

We defined a criterion of LCL ≥ 72 h (hereafter referred to as LCL72) to identify marine air masses that have been minimally influenced by land. Figure 2 shows examples of the backward trajectories calculated for the MR19-03C cruise between Japan and the Arctic and for the RITS94 cruise from North to South America, respectively. The light-blue and purple lines represent trajectories for marine and land-influenced air mass cases, respectively. The red lines indicate the cases in which the observed ozone mixing ratio was greater than 50 ppb (polluted).

The LCL72 criterion was evaluated using observed radon concentrations from ACE-1 (Whittlestone et al., 1998), ACE-Asia, ATOMIC, ICEALOT, NAAMES1–4, and WACS ship-borne observations as a tracer of land contact (Fig. 3). Radon, emitted from land and lost with a half-life of 3.8 d (Zhang et al., 2021), is suitable for testing the performance of 120 h trajectories and then removing the cases affected by fresh pollution. The median and third-quartile radon levels are diminished by almost two-thirds when LCL increases from 10 to 80 h, with a clear drop occurring between 60 and 80 h since the last contact with land. This provides the basis for a 72 h LCL threshold for identifying marine air masses with little or no influence from land.

Although the discrimination between oceanic and land-
influenced air masses is imperfect, largely due to the uncer-
tainties in the trajectory calculations, the agreement between
the LCL72 and the radon < 1000 mBq m⁻³ criteria (during
the campaigns for which this parameter was available) sug-
gests that it is reasonable to apply the LCL72 flag to all of
the data treated in this study over the global oceans as the
first filter against land influences. In subsequent studies on
this oceanic ozone dataset, other filters of land influence can
be developed and used to meet the requirements of the type
of analysis being undertaken. For example, a more stringent
criterion (radon < 100 mBq m⁻³) was used to select base-
line data at the Kennaook/Cape Grim station (Chambers et
al., 2018). The data that met the LCL72 criterion covered
161 037 h (77 % of the original dataset). Note that the data
with a LCL of less than 72 h are kept in the data file, as they
may be useful for other purposes.

## 2.2 Aircraft data

Table 2 lists the 48 airborne campaigns included in the toar2_oceans_Airborne_data_5000m_250203.csv dataset. The land mask mentioned in Sect. 2.1 was used to extract the airborne observations over the oceans. The high-latitude data (65–90° N or 90–60° S) were not masked and were used directly. The original merge-type data files from aircraft observations had different time resolutions, from < 10 to 90 s; particularly for old missions, only a coarse time resolution was available. Considering the temporal coverage and taking advantage of the relatively high temporal resolution of the more recent data, a variable temporal resolution in the range of 10–90 s was used. For campaigns where data with a higher temporal resolution (e.g., 1 Hz) were available, e.g., from FAAM measurements, the data were averaged over 10 s. A total of 424 005 and 252 086 data records for the respective altitudes of < 5000 and < 2000 m are included in the dataset, covering a period from 1987 to 2020. The data originated from the USA, the UK, and Germany/Canada, and they covered almost all global regions except for the R4 region (tropical Indian Ocean). Data for R5 (southern Indian Ocean) were sparse: only 62 points from the TRACE-A mission. The instruments used for the measurements of ozone are generally based on fast-response instruments, e.g., high-sensitivity chemiluminescence or UV absorption. Additional data on observations of pollutant tracers, i.e., CO, NO, and NO₂, were archived together (Table S2). The backward trajectories were applied to each measurement point, similar to the ship/buoy-based data, with the starting altitude set to the GPS altitude of the aircraft or to 500 m when it was lower. The proportions of the data meeting the LCL72 criterion were 74 % and 63 % for the < 5000 and < 2000 m cases, respectively. This was partly affected by the assumed top altitude boundary of “land” at 2500 m. The bottom panel of Fig. 1 shows the data meeting the LCL72 criterion at an altitude < 2000 m.
<table><caption>Table 1. List of cruise/buoy data contained in the ship/buoy data file.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager) (working group member worked on the data)</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S1</td><td>MR12- MR12- 02</td><td>Mirai</td><td>1 h</td><td>2012</td><td>733</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1</td><td>Kanaya et al. (2019)</td><td></td></tr><tr><td>S2</td><td>MR13- MR13- 04, 05, 06, 14-01, 02</td><td>Mirai</td><td>1 h</td><td>2013</td><td>2605</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 4, 10</td><td>Kanaya et al. (2019)</td><td></td></tr><tr><td>S3</td><td>MR14- MR14- 04, 05, 06</td><td>Mirai</td><td>1 h</td><td>2014</td><td>3561</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 4, 10</td><td>Kanaya et al. (2019)</td><td></td></tr><tr><td>S4</td><td>MR15- MR15- 03, 04, 05</td><td>Mirai</td><td>1 h</td><td>2015</td><td>2374</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 5, 10</td><td>Kanaya et al. (2019)</td><td></td></tr><tr><td>S5</td><td>MR16- MR16- 06, 08, 09</td><td>Mirai</td><td>1 h</td><td>2016</td><td>2393</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 3, 10, 11</td><td>Kanaya et al. (2019)</td><td></td></tr><tr><td>S6</td><td>MR17- MR17- 05C, 08</td><td>Mirai</td><td>1 h</td><td>2017</td><td>2662</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 4, 10</td><td>https://doi.org/10.17596/0001879 (JAMSTEC, 2017), https://doi.org/10.17596/0001881 (JAMSTEC, 2018a), https://doi.org/10.17596/0001882 (JAMSTEC, 2018b)</td><td></td></tr><tr><td>S7</td><td>MR18- MR18- 04, 05C, 06</td><td>Mirai</td><td>1 h</td><td>2018</td><td>2567</td><td>CO</td><td>Thermo, Model 49C</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 3, 10, 11</td><td>https://doi.org/10.17596/0001886 (JAMSTEC, 2018c), https://doi.org/10.17596/0001887 (JAMSTEC, 2018d), https://doi.org/10.17596/0001888 (JAMSTEC, 2018e), https://doi.org/10.17596/0001889 (JAMSTEC, 2019a), https://doi.org/10.17596/0001976 (JAMSTEC, 2019b)</td><td></td></tr><tr><td>S8</td><td>MR19- MR19- 03C, 04</td><td>Mirai</td><td>1 h</td><td>2019</td><td>2712</td><td>CO</td><td>2B Technologies, Model 205</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 4, 5, 10, 11</td><td>https://doi.org/10.17596/0002077 (JAMSTEC, 2019c), https://doi.org/10.17596/0002101 (JAMSTEC, 2019d), https://doi.org/10.17596/0002118 (JAMSTEC, 2020a)</td><td></td></tr><tr><td>S9</td><td>MR20- MR20- E01, 05C, E02, 01</td><td>Mirai</td><td>1 h</td><td>2020</td><td>2787</td><td>CO</td><td>2B Technologies, Model 205</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 10</td><td>https://doi.org/10.17596/0002152 (JAMSTEC, 2020b), https://doi.org/10.17596/0002165 (JAMSTEC, 2020c), https://doi.org/10.17596/0002191 (JAMSTEC, 2020d), https://doi.org/10.17596/0002121 (JAMSTEC, 2020e)</td><td></td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager) [working group member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S10</td><td>MR21: MR21-01.03, 05C, 06</td><td>Mirai</td><td>1 h</td><td>2021</td><td>2990</td><td>CO</td><td>2B Technologies, Model 205</td><td>1 %</td><td>Yugo Kanaya</td><td>R1, 2, 10</td><td></td><td>https://doi.org/10.17596/0002308 (JAMSTEC, 2021a), https://doi.org/10.17596/0002310 (JAMSTEC, 2021b), https://doi.org/10.17596/0002331 (JAMSTEC, 2021c), https://doi.org/10.17596/0002312 (JURCAOS & JAMSTEC, 2021), https://doi.org/10.17596/0002313 (JURCAOS & JAMSTEC, 2022)</td></tr><tr><td>S11</td><td>KH-18-6</td><td>Hakaho Maru</td><td>1 h</td><td>2018</td><td>527</td><td>CO</td><td>2B Technologies, Model 205</td><td>1 %</td><td>Yugo Kanaya</td><td>R4, 5</td><td>Ueda et al. (2023)</td><td></td></tr><tr><td>S12</td><td>NAAMES1</td><td>Atlantis</td><td>1 h</td><td>2015</td><td>525</td><td>CN</td><td>Thermo, Model 49C</td><td>± (2+5%) ppb</td><td>James Johnson</td><td>R7</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S13</td><td>NAAMES2</td><td>Atlantis</td><td>1 h</td><td>2016</td><td>529</td><td>CN</td><td>Thermo, Model 49C</td><td></td><td>James Johnson</td><td>R7</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S14</td><td>NAAMES3</td><td>Atlantis</td><td>1 h</td><td>2017</td><td>486</td><td>CN</td><td>Thermo, Model 49C</td><td></td><td>James Johnson</td><td>R7</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S15</td><td>NAAMES4</td><td>Atlantis</td><td>1 h</td><td>2018</td><td>497</td><td>CN</td><td>Thermo, Model 49C</td><td></td><td>James Johnson</td><td>R7, 8</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S16</td><td>ATOMIC</td><td>Ronald H. Brown</td><td>1 h</td><td>2020</td><td>695</td><td>CN</td><td>Thermo, Model 49C</td><td></td><td>James Johnson</td><td>R8</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S17</td><td>DYNAMO</td><td>Roger Revelle</td><td>1 h</td><td>2011</td><td>1130</td><td>CN</td><td>Thermo, Model 49C</td><td></td><td>James Johnson</td><td>R4, 5</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S18</td><td>WACS</td><td>Knorr</td><td>1 h</td><td>2014</td><td>192</td><td>CN</td><td>Thermo, Model 49C</td><td></td><td>James Johnson</td><td>R7</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S19</td><td>VOCALS</td><td>Ronald H. Brown</td><td>1 h</td><td>2008</td><td>745</td><td>CN</td><td>TECO 49</td><td></td><td>James Johnson</td><td>R2, 3</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S20</td><td>MAGE92</td><td>John V. Vickers</td><td>1 h</td><td>1992</td><td>670</td><td>CN</td><td>Dasibi 1008-AH</td><td>NA</td><td>James Johnson</td><td>R1, 2</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S21</td><td>RITS93</td><td>Surveyor</td><td>1 h</td><td>1993</td><td>939</td><td>CN</td><td>Dasibi 1008-AH</td><td>NA</td><td>James Johnson</td><td>R1, 2, 3, 9, 11</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S22</td><td>RITS94</td><td>Surveyor</td><td>1 h</td><td>1994</td><td>965</td><td>CN</td><td>Dasibi 1008-AH and TECO 49</td><td>NA</td><td>James Johnson</td><td>R1, 2, 3, 9, 11</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S23</td><td>ACE1</td><td>Discoverer</td><td>1 h</td><td>1995</td><td>1102</td><td>CN</td><td>Dasibi 1008-AH and TECO 49</td><td>NA</td><td>James Johnson</td><td>R1, 2, 3</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr><tr><td>S24</td><td>ACE-Asia</td><td>Ronald H. Brown</td><td>1 h</td><td>2001</td><td>808</td><td>CN</td><td>Dasibi 1008-AH and TECO 49</td><td>NA</td><td>James Johnson</td><td>R1</td><td></td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>P1 (data manager) (working group) member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S25</td><td>NEAQS 2002</td><td>Ronald H. Brown</td><td>1 h</td><td>2002</td><td>467</td><td>NO, NO<sub>2</sub></td><td>Dasibi 1008-AH and TECO 49</td><td>±(2% + 1 ppb)</td><td>(Kenneth Aikin)</td><td>R7</td><td></td><td>https://csl.noaa.gov/projects/neaqs/ (last access: 6 September 2025)</td></tr><tr><td>S26</td><td>NEAQS 2004</td><td>Ronald H. Brown</td><td>1 h</td><td>2004</td><td>699</td><td>CO, NO, NO<sub>2</sub></td><td>Dasibi 1008-AH and TECO 49</td><td>±(2 + 5%) ppb</td><td>(Kenneth Aikin)</td><td>R7</td><td></td><td>https://csl.noaa.gov/projects/2004/ (last access: 6 September 2025)</td></tr><tr><td>S27</td><td>TEXAQS 2006</td><td>Ronald H. Brown</td><td>1 h</td><td>2006</td><td>604</td><td>CO, NO, NO<sub>2</sub></td><td>TECO 49c</td><td>±(3% + 0.05) ppbv</td><td>(Kenneth Aikin)</td><td>R7</td><td></td><td>https://csl.noaa.gov/projects/2006/ (last access: 6 September 2025)</td></tr><tr><td>S28</td><td>ICEALOT</td><td>Knorr</td><td>1 h</td><td>2008</td><td>726</td><td>CO, NO, NO<sub>2</sub>, CN</td><td>TECO 49c</td><td>±(2% + 0.05) ppbv</td><td>Kenneth Aikin and James Johnson</td><td>R7, 10</td><td></td><td>https://csl.noaa.gov/groups/csl17/ measurements/2008ICEALOT/ (last access: 6 September 2025)</td></tr><tr><td>S29</td><td>CalNex 2010</td><td>Atlantis</td><td>1 h</td><td>2010</td><td>473</td><td>CO, NO, NO<sub>2</sub></td><td>Thermo Environmental 49c</td><td>±(2% + 1) ppb</td><td>(Kenneth Aikin)</td><td>R1</td><td></td><td>https://csl.noaa.gov/projects/calnex/ (last access: 6 September 2025)</td></tr><tr><td>S30</td><td>MALASPINA</td><td>Hesperides</td><td>1 h</td><td>2010</td><td>3733</td><td>NA</td><td>UV absorption/2B Technologies, Model 205</td><td>NA</td><td>Alfonso Sáiz-López</td><td>R1, 2, 3, 5, 7, 8, 9</td><td>Prados-Roman et al. (2015)</td><td></td></tr><tr><td>S31</td><td>DRAKE2009</td><td>Polarstern</td><td>1 h</td><td>2009</td><td>215</td><td>NA</td><td>UV absorption/2B Technologies</td><td>NA</td><td>[Theodore Koenig]</td><td>R9</td><td></td><td>https://csl.noaa.gov/report/21000666 (last access: 6 September 2025)</td></tr><tr><td>S32</td><td>MAP-IO/ SWING 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>938</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/ (last access: 6 September 2025) https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr><tr><td>S33</td><td>MAP IO/ OP1 TAAF 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>630</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R4, 5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr><tr><td>S34</td><td>MAP IO/ SCRATCH 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>342</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R4, 5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr><tr><td>S35</td><td>MAP IO/ OP2 TAAF 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>593</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R4, 5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr><tr><td>S36</td><td>MAP IO/MAYOBS 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>438</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R4, 5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr><tr><td>S37</td><td>MAP IO/OP3 TAAF 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>600</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr><tr><td>S38</td><td>MAP IO/OP4 TAAF 2021</td><td>Marion Dufresne</td><td>1 h</td><td>2021</td><td>549</td><td>NA</td><td>HORIBA APOA-370</td><td>NA</td><td>Aurélie Colomb and Pierre Tulet</td><td>R4, 5</td><td>Tulet et al. (2024)</td><td>http://www.mapio.re/https://www.aeris-data.fr/catalogue-map-io/ (last access: 6 September 2025)</td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager) [working group member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S39</td><td>Ka'imimoana</td><td>Ka'imimoana</td><td>1 h</td><td>2012</td><td>505</td><td>NA</td><td></td><td>NA</td><td>Rainer Volkamer and Theodore Koenig</td><td>R2, 3</td><td>Coburn et al. (2014)</td><td>https://www.eol.ucar.edu/field-projects/torero (last access: 6 September 2025)</td></tr><tr><td>S40</td><td>DWD-MPI</td><td>Meteor, 1 h</td><td>1977–2002</td><td>103 352</td><td>NA</td><td></td><td>1977–1996: a wet chemical instrument using the potassium iodide (KI) method; 1995–2002: Thermo Instrument UV absorption, 2002: Thermo Environmental, 49 and 49c</td><td>1977–1996: ±5 ppb; 1995–2002: 6% ± 2 ppb; 2002: &lt; 5 %</td><td>[Theodore Koenig]</td><td>R3, 5, 7, 8, 9, 10, 11</td><td>Lelieveld et al. (2004)</td><td></td></tr><tr><td></td><td></td><td>Po, larstern, Walther</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Herwig; Anton</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Dohrm; Ymer</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Academie</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Fe-dorov</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>(DWD; 1977–1996)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>+</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Berlin</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Express</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>(MPI; 1995–2002)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>Meteor</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>(MPI; 2002)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager)<br>[working group<br>member worked on the<br>data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S41</td><td>17v01</td><td>Investigator</td><td>1 h</td><td>2017</td><td>1067</td><td>N/A</td><td>Thermo<br>Scientific,<br>Model 49i<br>analyzer ×2</td><td>2.3%-2.9%</td><td>Suzie Molloy</td><td>R3, 11</td><td></td><td>https://data.csiro.au/ (last access: 6<br>September 2025)</td></tr><tr><td>S42</td><td>17v02</td><td>Investigator</td><td>1 h</td><td>2017</td><td>223</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S43</td><td>17v03</td><td>Investigator</td><td>1 h</td><td>2017</td><td>729</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S44</td><td>17v04</td><td>Investigator</td><td>1 h</td><td>2017</td><td>412</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S45</td><td>17v05</td><td>Investigator</td><td>1 h</td><td>2017</td><td>670</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S46</td><td>18v01</td><td>Investigator</td><td>1 h</td><td>2018</td><td>864</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R2, 3, 5</td><td></td><td></td></tr><tr><td>S47</td><td>18v02</td><td>Investigator</td><td>1 h</td><td>2018</td><td>359</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3, 11</td><td></td><td></td></tr><tr><td>S48</td><td>18v03</td><td>Investigator</td><td>1 h</td><td>2018</td><td>397</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S49</td><td>18v04</td><td>Investigator</td><td>1 h</td><td>2018</td><td>588</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S50</td><td>18v05</td><td>Investigator</td><td>1 h</td><td>2018</td><td>657</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S51</td><td>18v06</td><td>Investigator</td><td>1 h</td><td>2018</td><td>581</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S52</td><td>18v08</td><td>Investigator</td><td>1 h</td><td>2018</td><td>284</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S53</td><td>19v01</td><td>Investigator</td><td>1 h</td><td>2019</td><td>848</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3, 11</td><td></td><td></td></tr><tr><td>S54</td><td>19v02</td><td>Investigator</td><td>1 h</td><td>2019</td><td>425</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R3</td><td></td><td></td></tr><tr><td>S55</td><td>19v03</td><td>Investigator</td><td>1 h</td><td>2019</td><td>667</td><td>NA</td><td>Thermo<br>Scientific,<br>Model 49i ×2</td><td></td><td>Suzie Molloy</td><td>R2, 3, 5</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager) [working group member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S56</td><td>IIOE2</td><td>Sagar Nidhi</td><td>1 h</td><td>2015</td><td>211</td><td>NA</td><td>Ecotech EC9810B</td><td>1 ppbv</td><td>Anoop Mahajan</td><td>R4, 5</td><td>Mahajan et al. (2019), Inamdar et al. (2020)</td><td></td></tr><tr><td>S57</td><td>SOE9</td><td>S. A. Agulhas</td><td>1 h</td><td>2016</td><td>1121</td><td>NA</td><td>Ecotech EC9810B</td><td>1 ppbv</td><td>Anoop Mahajan</td><td>R5, 11</td><td></td><td></td></tr><tr><td>S58</td><td>SOE11</td><td>S. A. Agulhas</td><td>1 h</td><td>2020</td><td>824</td><td>NA</td><td>Ecotech EC9810B</td><td>1 ppbv</td><td>Anoop Mahajan</td><td>R5, 11</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager) [working group member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>B1</td><td>O-Buoy01</td><td>O-Buoy</td><td>1 h</td><td>2009–2010</td><td>4741</td><td>NA</td><td>Custom-built 2B Technologies, Model 205 dual-beam ozone monitors</td><td>Manufacturer-specified limit of detection of 1 ppb; individual measurement uncertainty was calculated to range from 2.1 to 3.5 ppb</td><td>John W. Halfacre</td><td>R10</td><td>Halfacre et al. (2014)</td><td></td></tr><tr><td>B2</td><td>O-Buoy02</td><td>O-Buoy</td><td>1 h</td><td>2010–2011</td><td>2832</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B3</td><td>O-Buoy03</td><td>O-Buoy</td><td>1 h</td><td>2010–2011</td><td>1405</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B4</td><td>O-Buoy04</td><td>O-Buoy</td><td>1 h</td><td>2010–2012</td><td>4424</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B5</td><td>O-Buoy05</td><td>O-Buoy</td><td>1 h</td><td>2011–2012</td><td>1036</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B6</td><td>O-Buoy06</td><td>O-Buoy</td><td>1 h</td><td>2012</td><td>338</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B7</td><td>O-Buoy07</td><td>O-Buoy</td><td>1 h</td><td>2012–2013</td><td>955</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B8</td><td>O-Buoy08</td><td>O-Buoy</td><td>1 h</td><td>2012–2016</td><td>2823</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B9</td><td>O-Buoy09</td><td>O-Buoy</td><td>1 h</td><td>2013</td><td>46</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B10</td><td>O-Buoy10</td><td>O-Buoy</td><td>1 h</td><td>2013–2014</td><td>3777</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B11</td><td>O-Buoy11</td><td>O-Buoy</td><td>1 h</td><td>2014–2015</td><td>3338</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B12</td><td>O-Buoy12</td><td>O-Buoy</td><td>1 h</td><td>2014–2015</td><td>851</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B13</td><td>O-Buoy13</td><td>O-Buoy</td><td>1 h</td><td>2015–2016</td><td>1881</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B14</td><td>O-Buoy14</td><td>O-Buoy</td><td>1 h</td><td>2015–2017</td><td>6229</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>B15</td><td>O-Buoy15</td><td>O-Buoy</td><td>1 h</td><td>2015–2016</td><td>389</td><td>NA</td><td></td><td></td><td>John W. Halfacre</td><td>R10</td><td></td><td></td></tr><tr><td>S59</td><td>YES-AQ</td><td>Gisang1</td><td>1 h</td><td>2015–2021</td><td>2156</td><td>NA</td><td>Thermo. Model 49C</td><td>1 ppb</td><td>Junsu Gil</td><td>R1</td><td>Ahn et al. (2024)</td><td></td></tr><tr><td>S60</td><td>MOSAIC</td><td>Polarstern</td><td>1 h</td><td>2019–2020</td><td>8131</td><td>CO</td><td>Thermo Fisher Scientific, Model 49i/49C; 2B Technologies, Model 205</td><td>Manufacturer-specified precision of 1.0 ppb for 20 s averages. CO 1.5 ppb (5 min)</td><td>Julia Schmale</td><td>R10</td><td>Angot et al. (2022)</td><td>https://doi.pangaea.de/10.1594/PANGAEA.944393 (Angot et al., 2022b), https://doi.pangaea.de/10.1594/PANGAEA.944389 (Angot et al., 2022c)</td></tr></tbody></table>
<table><caption>Table 1. Continued.</caption><thead><tr><th>Label</th><th>Cruise</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data manager) [working group member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>S61</td><td>SAGA3</td><td>Korolev</td><td>1 h</td><td>1990</td><td>562</td><td>NA</td><td>Dasibi<br>1008-AH</td><td>±3 ppbv</td><td>James Johnson</td><td>R8</td><td>Thompson et al. (1993)</td><td>https://saga.pmel.noaa.gov/data/ (last access: 6 September 2025)</td></tr><tr><td>S62</td><td>AEROSOLS99- INDOEX</td><td>Ronald H. Brown</td><td>1 h</td><td>1999</td><td>1392</td><td>CN</td><td>Dasibi<br>1008-AH and TECO 49</td><td>NA</td><td>James Johnson</td><td>R4, 5, 7, 8, 9</td><td>https://www.pmel.noaa.gov/acg/data/ (last access: 6 September 2025)</td></tr></tbody></table>

Note that NA represents “not available”.

## 2.3 Ozonesonde data

A total of 29 selected ozonesonde launch sites/campaigns are included in the toar2_oceans_ozonesondedata_250203.csv dataset. The sites are listed in Table S3 and shown in Fig. 1. There are no data for regions R4 (Indian Ocean) and R9 (South Atlantic). As the availability of geopotential height information was considered a high priority in the creation of the dataset, data from earlier dates when this parameter was not available (e.g., Alert before 2000) were not included. Their inclusion could be a future task. The ozone mixing ratio was calculated from the atmospheric pressure and the ozone partial pressure data. To reduce the data volume, one data point every 200 m (data closest to the top of each layer, e.g., near 200 or 400 m) was extracted up to a 5000 m altitude. The (1/e) ozone sensor response time (~30 s) gives the ozonesonde a vertical resolution of about 150 m for a typical balloon ascent rate (Van Malderen et al., 2025). Most of the sites (24 out of 29) were taken from the homogenized HEGIFTOM dataset (Van Malderen et al., 2025) to ensure data quality. The selected launch sites are on islands or close to the coast. The other five data sources are from island and shipboard campaigns in the tropical Pacific (Gómez Martín et al., 2016; Shiotani et al., 2002; Fujiwara et al., 2003), with the addition of three datasets in R11 (Antarctic region) from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ozonesonde database to improve the coverage. The data are all from electrochemical concentration cell (ECC)-type ozonesondes. In total, 666 470 and 254 276 data points below respective altitudes of 5000 and 2000 m were collected.

Backward-trajectory calculations were only performed for selected heights (500, 1000, 1500, 2000, 3000, 4000, and 5000 m) to reduce the computational cost. The LCL information relative to the closest of the height points was used. The proportions of the data meeting the LCL72 criterion were 80% and 67% for the full dataset (< 5000 m altitude) and for the < 2000 m data subset, respectively. As the launch site is usually on land, the latitude/longitude information from the backward trajectories at 0 and 1 h prior to launch was not included in the LCL calculation. Therefore, the potential influence of local air pollution in the vicinity of the launch site needs to be considered when using these data. For a further characterization of air masses and screening, wind direction sector information (Table S3), constructed from the coordinates from the backward-trajectory files at 0 and 1 h before launch, was added to the dataset.

## 2.4 Non-polar coastal site data

The list of 21 non-polar coastal sites included in the toar2_oceans_coastalsites_250203.csv file is shown in Table S4, and their locations are given in Fig. 1: 16 sites are from the TOAR-II database (Schröder et al., 2024), 2 are from field campaigns, 2 are from the EANET monitoring net-
<table><caption>Table 2. List of aircraft-based campaign data contained in the aircraft dataset.</caption><thead><tr><th>Label</th><th>Campaign</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number<br>&lt; 5000 m<br>&lt; 2000 m</th><th>Data number<br>&lt; 5000 m</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>PI (data<br>manager)<br>(working group<br>member<br>worked on the<br>data)</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>A</td><td>ABLE-2B</td><td>Electra</td><td>60 s</td><td>1987</td><td>67</td><td>677</td><td>CO</td><td>NA</td><td>NA</td><td>(Gao Chen)</td><td>R7.8</td><td>Harris et al. (1990)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#ABLE-2B (last access: 6 September 2025)</td></tr><tr><td>A2</td><td>ABLE-3A</td><td>Electra</td><td>60 s</td><td>1988</td><td>1668</td><td>3824</td><td>NO</td><td>NA</td><td>2 ppbv (detection limit)</td><td>(Gao Chen)</td><td>R1.7.10</td><td>Harris et al. (1992)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#ABLE-3A (last access: 6 September 2025)</td></tr><tr><td>A3</td><td>ABLE-3B</td><td>Electra</td><td>90 s</td><td>1990</td><td>133</td><td>258</td><td>CO, NO, NO<sub>2</sub></td><td>NA</td><td>NA</td><td>(Gao Chen)</td><td>R7.10</td><td>Harris et al. (1994)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#ABLE-3B (last access: 6 September 2025)</td></tr><tr><td>A4</td><td>CITE-3</td><td>Electra</td><td>10 s</td><td>1989</td><td>21 355</td><td>28 718</td><td>CO</td><td>Chemiluminescence</td><td>N/A</td><td>(Gao Chen)</td><td>R7.8</td><td>Hoell et al. (1993)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#CITE-3 (last access: 6 September 2025)</td></tr><tr><td>A5</td><td>PEM-West A</td><td>DC-8</td><td>90 s</td><td>1991</td><td>801</td><td>1511</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>5% or 2 ppb</td><td>(Gao Chen)</td><td>R1.2</td><td>Hoell et al. (1996)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#PEMWEST-A (last access: 6 September 2025)</td></tr><tr><td>A6</td><td>PEM-West B</td><td>DC-8</td><td>30 s</td><td>1994</td><td>2142</td><td>5259</td><td>CO, NO</td><td>Chemiluminescence</td><td>3% or 2 ppb</td><td>(Gao Chen)</td><td>R1.2</td><td>Hoell et al. (1997)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#PEMWEST-B (last access: 6 September 2025)</td></tr><tr><td>A7</td><td>TRACE-A</td><td>DC-8</td><td>90 s</td><td>1992</td><td>131</td><td>408</td><td>CO, NO, NO<sub>2</sub></td><td>NA</td><td>NA</td><td>(Gao Chen)</td><td>R5.7.8.9</td><td>Fislman et al. (1996)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-A (last access: 6 September 2025)</td></tr><tr><td>A8</td><td>PEM-Tropics A</td><td>DC-8</td><td>60 s</td><td>1996</td><td>1395</td><td>2969</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>3% or 2 ppb</td><td>(Gao Chen)</td><td>R1.2.3.11</td><td>Hoell et al. (1999)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#PEMTROPICS-A (last access: 6 September 2025)</td></tr><tr><td>A9</td><td>PEM-Tropics B P3B</td><td>P3B</td><td>60 s</td><td>1999</td><td>3111</td><td>4939</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>3% or 2 ppb</td><td>(Gao Chen)</td><td>R1.2.3.7</td><td>Ruper et al. (2001)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#PEMTROPICS-B (last access: 6 September 2025)</td></tr><tr><td>A10</td><td>PEM-Tropics B DC8</td><td>DC-8</td><td>60 s</td><td>1999</td><td>1573</td><td>3027</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>3% or 2 ppb</td><td>(Gao Chen)</td><td>R1.2.3.8</td><td>Jacob et al. (2003)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr><tr><td>A11</td><td>TRACE-P P3B</td><td>P3B</td><td>60 s</td><td>2001</td><td>3036</td><td>6374</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>5% or 2 ppb</td><td>(Gao Chen)</td><td>R1.2</td><td>Singh et al. (2006)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr><tr><td>A12</td><td>TRACE-P DC8</td><td>DC-8</td><td>60 s</td><td>2001</td><td>2024</td><td>3925</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>5% or 2 ppb</td><td>(Gao Chen)</td><td>R1.7</td><td>https://www.gte.larc.nasa.gov/missions/ntex/binextxhtml (last access: 6 September 2025)</td><td>https://www.gte.larc.nasa.gov/missions/ntex/binextxhtml (last access: 6 September 2025)</td></tr><tr><td>A13</td><td>INTEX-NA</td><td>DC-8</td><td>60 s</td><td>2004</td><td>1056</td><td>1739</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>5% or 1 ppb</td><td>(Gao Chen)</td><td>R1.2.7</td><td>Singh et al. (2009)</td><td>https://www.gte.larc.nasa.gov/missions/ntex/binextxhtml (last access: 6 September 2025)</td></tr><tr><td>A14</td><td>INTEX-B DC8</td><td>DC-8</td><td>60 s</td><td>2006</td><td>1262</td><td>2695</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>1 ppb or 5%</td><td>(Gao Chen)</td><td>R1.2.7</td><td>Shon et al. (2008), Kleb et al. (2011)</td><td>https://www.gte.larc.nasa.gov/missions/ntex/binextxhtml (last access: 6 September 2025)</td></tr><tr><td>A15</td><td>INTEX-B C-130</td><td>C-130</td><td>60 s</td><td>2006</td><td>741</td><td>2468</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>0.1 ppbv or 5%</td><td>(Gao Chen)</td><td>R1.2.7</td><td>Jacob et al. (2010)</td><td>https://www.gte.larc.nasa.gov/missions/ntex/binextxhtml (last access: 6 September 2025)</td></tr><tr><td>A16</td><td>ARCTAS</td><td>DC-8</td><td>60 s</td><td>2008</td><td>1287</td><td>2444</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>±2 ppbv</td><td>Andrew<br>Weinheimer,<br>Denise<br>Moniska,<br>David Knapp,<br>and Ilama<br>Pollack</td><td>R1.10</td><td>Jacob et al. (2010)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr><tr><td>A17</td><td>SEAC-4RS DC8</td><td>DC-8</td><td>60 s</td><td>2013</td><td>376</td><td>601</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>0.030 ppbv + 3%</td><td>Tom Ryerson,<br>Jeff Pressel,<br>and Ilama<br>Pollack</td><td>R1.7</td><td>Shon et al. (2008), Kleb et al. (2011)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr><tr><td>A18</td><td>SEAC-4RS ER2</td><td>ER2</td><td>60 s</td><td>2013</td><td>83</td><td>202</td><td>CO</td><td>UV absorption</td><td>3% ± precision</td><td>(Gao Chen)</td><td>R7</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr><tr><td>A19</td><td>DISCOVER-AQ</td><td>P3B</td><td>60 s</td><td>2013</td><td>145</td><td>162</td><td>NO, NO<sub>2</sub></td><td>Four-channel<br>chemiluminescence</td><td>0.1 ppbv + 5%</td><td>(Gao Chen)</td><td>R1</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr><tr><td>A20</td><td>KORUS-AQ</td><td>DC-8</td><td>60 s</td><td>2016</td><td>2057</td><td>2631</td><td>CO, NO, NO<sub>2</sub></td><td>Four-channel<br>chemiluminescence,</td><td>5 ppbv + 10%</td><td>(Gao Chen)</td><td>R1</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td><td>https://www.gte.larc.nasa.gov/gte_mrg1.htm#TRACE-P (last access: 6 September 2025)</td></tr></tbody></table>
<table><caption>Table 2. Continued.</caption><thead><tr><th>Label</th><th>Campaign</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number &lt; 2000 m</th><th>Data number &lt; 5000 m</th><th>Ancillary data</th><th>Instrument</th><th>L uncertainty</th><th>P1 (data manager) [working group member worked on the data]</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>A21</td><td>ATom1-4</td><td>DC-8</td><td>10 s</td><td>2016-2018</td><td>23715</td><td>42975</td><td>CO, NO, NO<sub>2</sub></td><td>Four-channel chemiluminescence</td><td>5-10 ppt</td><td>Ilaam Bourgeois, Jeff Peschli, and Chelsea Thompson</td><td>R1, 2, 3, 7, 8, 9, 10, 11</td><td>Wofsy et al. (2021)</td><td>https://ilac.ornl.gov/cgi-bin/viewer.pl?db_id=1881 (last access: 6 September 2025)</td></tr><tr><td>A22</td><td>HIPPO</td><td>NSF NCAR G-V</td><td>10 s</td><td>2009-2011</td><td>20 509</td><td>45 018</td><td>CO</td><td>Ultraviolet absorption</td><td>Final data: accuracy approximately ±5%</td><td>David Faley, Ru-Shan Gao, and J. Ryan Spackman</td><td>R1, 2, 3, 7, 8, 10, 11</td><td>Wofsy et al. (2021)</td><td>https://www.ecol.acar.edu/field-projects/hippo (last access: 6 September 2025)</td></tr><tr><td>A23</td><td>ACSIS FAAM</td><td>FAAMIBAe 146- 10 s 301</td><td>2017-2020</td><td>23 046</td><td>34 840</td><td>CO</td><td>TECO 49</td><td>N/A</td><td></td><td>James Lee</td><td>R7, 8</td><td></td><td>https://catalogue.ceda.ac.uk/unit/8d2e81db2499983aa87781b3d45a (last access: 6 September 2025); https://www.laam.ac.uk/sphint/coredata/dynamic_content/modules/himitezoone (last access: 6 September 2025)</td></tr><tr><td>A24</td><td>ACCACIA FAAM</td><td>FAAMIBAe 146- 10 s 301</td><td>2013</td><td>7548</td><td>11 369</td><td>CO</td><td></td><td></td><td></td><td>James Lee</td><td>R10</td><td></td><td></td></tr><tr><td>A25</td><td>CAST FAAM</td><td>FAAMIBAe 146- 10 s 301</td><td>2014</td><td>7210</td><td>10 566</td><td>CO</td><td></td><td></td><td></td><td>James Lee</td><td>R1</td><td></td><td></td></tr><tr><td>A26</td><td>CLARIFY FAAM</td><td>FAAMIBAe 146- 10 s 301</td><td>2017</td><td>14 248</td><td>24 319</td><td>CO</td><td></td><td></td><td></td><td>James Lee</td><td>R8</td><td></td><td></td></tr><tr><td>A27</td><td>ITOP FAAM</td><td>FAAMIBAe 146- 10 s 301</td><td>2004</td><td>4683</td><td>13 350</td><td>CO</td><td></td><td></td><td></td><td>James Lee</td><td>R7</td><td></td><td></td></tr><tr><td>A28</td><td>VOCALS FAAM</td><td>FAAMIBAe 146- 10 s 301</td><td>2008</td><td>19 037</td><td>19 736</td><td>CO</td><td></td><td></td><td></td><td>James Lee</td><td>R2, 3</td><td></td><td></td></tr><tr><td>A29</td><td>NAREI 1996</td><td>NOAA P-3</td><td>10 s</td><td>1996</td><td>3006</td><td>4783</td><td>CO, NO, NO<sub>2</sub></td><td>NA</td><td>NA</td><td>(Kenneth Aikin)</td><td>R7</td><td></td><td></td></tr><tr><td>A30</td><td>NAREI 1997</td><td>NOAA P-3</td><td>10 s</td><td>1997</td><td>4886</td><td>9931</td><td>CO, NO, NO<sub>2</sub></td><td>NA</td><td>NA</td><td>(Kenneth Aikin)</td><td>R7</td><td></td><td></td></tr><tr><td>A31</td><td>TEXAQS2000</td><td>NCAR Electra</td><td>10 s</td><td>2000</td><td>2496</td><td>3904</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>NA</td><td>(Kenneth Aikin)</td><td>R7</td><td>Ryerson et al. (1998)</td><td>https://csl.noa.gov/projects/txcap2k/ (last access: 6 September 2025)</td></tr><tr><td>A32</td><td>ITCT2002</td><td>NOAA WP-3D</td><td>10 s</td><td>2002</td><td>6004</td><td>13 772</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>±2%</td><td>(Kenneth Aikin)</td><td>R1, 7</td><td>https://csl.noa.gov/projects/itct/2k2/ (last access: 6 September 2025)</td><td></td></tr><tr><td>A33</td><td>ITCT2004</td><td>NOAA WP-3D</td><td>10 s</td><td>2004</td><td>15 213</td><td>19 791</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>0.1 + 3%)</td><td>(Kenneth Aikin)</td><td>R7</td><td>https://csl.noa.gov/projects/2004/ (last access: 6 September 2025)</td><td></td></tr><tr><td>A34</td><td>HURRICANE2006</td><td>NOAA G-4</td><td>10 s</td><td>2006</td><td>334</td><td>663</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth Aikin)</td><td>R7, 8</td><td>https://csl.noa.gov/groups/esl/measurements/2006/hurricane/ (last access: 6 September 2025)</td><td></td></tr><tr><td>A35</td><td>TEXAQS2006</td><td>NOAA WP-3D</td><td>10 s</td><td>2006</td><td>5395</td><td>6772</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>0.050 ppbv + 3%</td><td>(Kenneth Aikin)</td><td>R7</td><td>https://csl.noa.gov/projects/2006/ (last access: 6 September 2025)</td><td></td></tr><tr><td>A36</td><td>ARCPAC2008</td><td>NOAA WP-3D</td><td>10 s</td><td>2008</td><td>4878</td><td>11 563</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>0.05 + 4%</td><td>(Kenneth Aikin)</td><td>R10</td><td>https://csl.noa.gov/projects/arcpac/ (last access: 6 September 2025)</td><td></td></tr><tr><td>A37</td><td>CalNex2010</td><td>NOAA WP-3D</td><td>10 s</td><td>2010</td><td>7040</td><td>9265</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>0.015 ppbv + 2%</td><td>(Kenneth Aikin)</td><td>R1, 7</td><td>Pollack et al. (2010)</td><td>https://csl.noa.gov/projects/calnex/ (last access: 6 September 2025)</td></tr><tr><td>A38</td><td>WINTERSTORMS2001</td><td>NA</td><td>10 s</td><td>2001</td><td>164</td><td>895</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth Aikin)</td><td>R1</td><td></td><td></td></tr><tr><td>A39</td><td>WINTERSTORMS2002</td><td>NA</td><td>10 s</td><td>2002</td><td>815</td><td>1867</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth Aikin)</td><td>R1, 7</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 2. Continued.</caption><thead><tr><th>Label</th><th>Campaign</th><th>Platform</th><th>Resolution</th><th>Year</th><th>Data number<br>&lt; 2000 m</th><th>Data number<br>&lt; 5000 m</th><th>Ancillary data</th><th>Instrument</th><th>Uncertainty</th><th>P1 (data<br>manager)<br>(working group<br>member<br>worked on the<br>data)</th><th>Regions</th><th>Literature</th><th>Data source</th></tr></thead><tbody><tr><td>A40</td><td>WINTERSTORMS2003</td><td>NOAA<br>G-4</td><td>10 s</td><td>2003</td><td>657</td><td>2225</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth<br>Aikin)</td><td>R1, 7</td><td></td><td>https://csl.noaa.gov/groups/csl7/<br>measurements/2003WinterStorms/ (last<br>access: 6 September 2025)</td></tr><tr><td>A41</td><td>WINTERSTORMS2004</td><td>NOAA<br>G-4</td><td>10 s</td><td>2004</td><td>992</td><td>3283</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth<br>Aikin)</td><td>R1, 2, 7</td><td></td><td>https://csl.noaa.gov/groups/csl7/<br>measurements/2004WinterStorms/ (last<br>access: 6 September 2025)</td></tr><tr><td>A42</td><td>WINTERSTORMS2005</td><td>NOAA<br>G-4</td><td>10 s</td><td>2005</td><td>541</td><td>2099</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth<br>Aikin)</td><td>R1</td><td></td><td>https://csl.noaa.gov/groups/csl7/<br>measurements/2005WinterStorms/ (last<br>access: 6 September 2025)</td></tr><tr><td>A43</td><td>WINTERSTORMS2006</td><td>NOAA<br>G-4</td><td>10 s</td><td>2006</td><td>900</td><td>1633</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth<br>Aikin)</td><td>R1</td><td></td><td>https://csl.noaa.gov/groups/csl7/<br>measurements/2006WinterStorms/ (last<br>access: 6 September 2025)</td></tr><tr><td>A44</td><td>WINTERSTORMS2007</td><td>NOAA<br>G-4</td><td>10 s</td><td>2007</td><td>355</td><td>1375</td><td>none</td><td>NA</td><td>NA</td><td>(Kenneth<br>Aikin)</td><td>R1, 7</td><td></td><td>https://csl.noaa.gov/groups/csl7/<br>measurements/2007WinterStorms/ (last<br>access: 6 September 2025)</td></tr><tr><td>A45</td><td>ACTIVATE</td><td>Falcon</td><td>60 s</td><td>2020</td><td>5983</td><td>6797</td><td>CO</td><td>UV absorption</td><td>greater of ±5 ppbv or<br>±5 %</td><td>(Gao Chen)</td><td>R7</td><td></td><td>https://www-air.larc.nasa.gov/cgi-bin/<br>ArcView/activate.2022TU25=1 (last<br>access: 6 September 2025)</td></tr><tr><td>A46</td><td>CONTRAST</td><td>NSF<br>NCAR<br>G-V</td><td>10 s</td><td>2014</td><td>3308</td><td>6318</td><td>CO, NO, NO<sub>2</sub></td><td>Chemiluminescence</td><td>NA</td><td>Rainer<br>Volkamer</td><td>R1, 2</td><td>Pan et al. (2017)</td><td>https:<br>/www.ecol.ucar.edu/field/projects/contrast<br>(last access: 6 September 2025),<br>https://doi.org/10.5194/csl7/<br>(Weinheimer, 2015)</td></tr><tr><td>A47</td><td>TORERO</td><td>NSF<br>NCAR<br>G-V</td><td>10 s</td><td>2012</td><td>7129</td><td>12,569</td><td>CO</td><td>Dual-channel UV<br>absorption<br>spectrometer</td><td>3 % + precision</td><td>Rainer<br>Volkamer</td><td>R2, 3, 8</td><td>Volkamer et al. (2015)</td><td>https:<br>/www.ecol.ucar.edu/field/projects/contrast<br>(last access: 6 September 2025),<br>https://doi.org/10.5194/csl7/<br>(Weinheimer, 2015)</td></tr><tr><td>A48</td><td>NETCARE</td><td>Polar 6</td><td>10 s</td><td>2014–2015</td><td>17,551</td><td>27,496</td><td>none</td><td>Thermo Scientific.<br>Model 49<sub>g</sub></td><td>NA</td><td>Ralf Staebler</td><td>R10</td><td>Abbatt et al. (2019)</td><td>https://open.canada.ca/data/en/dataset/<br>11434724, 6c73, 485c, bc2b, a3d531996, le9<br>(last access: 6 September 2025),<br>https://open.canada.ca/data/en/dataset/<br>efcbe41c-4890d-404d-bb1b-421456022d51<br>(last access: 6 September 2025)</td></tr></tbody></table>

Note that NA represents “not available”.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Backward trajectories (120 h long) for observations with oceanic (light blue) and apparently land-influenced (purple) conditions, as assessed with the LCL72 criterion during (**a**) the MR19-03C observations from 29 September to 10 November 2019 and (**b**) the RITS94 observations from 23 November 1993 to 6 January 1994. The red lines indicate cases in which the observed ozone mixing ratio is greater than 50 ppb.</figcaption></figure>

work, and 1 is from CSIRO. The sites were selected on the basis of the availability of high-quality data over long periods (typically > 10 years) and for the global coverage. However, no sites matching these criteria could be found for regions R4 and R5 (Indian Ocean). The Kennaook/Cape Grim dataset available on the TOAR-II database was not used; rather, an-

other dataset provided by CSIRO was employed. The latter was an updated version, extended through to the end of 2020, with the years 1982–2017 inclusive being fully QA/QC data on the WMO GAW/BiPM scale. The 2018–2020 period was in the final stages of QA/QC, and the fully finalized dataset has subsequently been published on EBAS (EBAS, 2025).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Decrease in radon concentrations with last contact with land from backward-trajectory analysis. ACE-1, ACE-Asia, ATOMIC, ICEALOT, NAAMES1-4, and WACS data were used in combination. Radon data were from NOAA PMEL. The dotted blue line indicates the adopted LCL72 criterion. Boxes and whiskers represent the 10th, 25th, 50th, 75th, and 90th percentiles.</figcaption></figure>

Further information on the instruments and uncertainties for each site can be found in the TOAR-II database. Obviously, the incorrect data (i.e., zero or negative values) have been removed, resulting in a total of 3 650 267 hourly observations being included. The LCL information based on the backward trajectories is included. To reduce the computational cost, only 6 hourly calculations were performed, and the result at the closest data timestamp was used. It should be noted that the risk of the influence from local air pollution is similar to that of the ozonesonde dataset. Data from Trinidad Head can be screened using the local wind direction information (as shown in Table S3).

## 2.5 Polar site data

The list of 17 polar coastal sites included in the toar2_oceans_polarsites_250203.csv file is shown in Table S5 and Fig. 1. Except for Alert and Belgrano stations, where the data came from the Canadian data site and from the National Institute for Aerospace Technology (INTA), respectively, the 15 datasets are from the TOAR-II database. A total of 3 362 716 hourly observations were included. Similarly to the case of the non-polar coastal sites, the LCL information based on the backward trajectories is included. To save computational cost, only 6-hourly trajectory calculations were performed, and the result at the closest data timestamp was used.

# 3 Data overview

In this section, some basic data analysis and descriptive statistics of the collected datasets are described, for informational purposes. Detailed discussion of the spatiotemporal distribution of tropospheric ozone and its trends over the oceans and polar regions will be presented in the assessment paper (Sommariva et al., 2025).

## 3.1 Latitudinal, longitudinal, and vertical transects

Figure 4 shows the latitudinal and longitudinal cross sections of the ship/buoy data after application of the LCL72 filter. The data are grouped into 10° latitudinal and 20° longitudinal bins. The median values in the Southern Hemisphere are in the range of 15.2–19.1 ppb, while those in the Northern Hemisphere are in the range of 20.5–34.0 ppb. As expected, a maximum median value was found between 25 and 55° N, where the ozone is photochemically produced from precursors anthropogenically emitted over the continents and transported over long distances to the open oceans (Fig. 1; see also Kanaya et al., 2019). The longitudinal distribution has less variability, with median values in a narrow range of ca. 20–30 ppb. The high episodes (higher than the 75th percentile) are evident from 35 to 45° N and from 120 to 140° E, suggesting that the effects of Asian pollution remain in the dataset, consistent with the discussion regarding the LCL72 filter.

Figure 5 shows the vertical profiles of the combined aircraft and ozonesonde data after application of the LCL72 filter. The data are grouped into 250 m altitude bins. The general tendency is that ozone mixing ratios increase with height, except in R7 (northern Atlantic), where the minimum median values occur in the 700–950 m layer, and in R8 (Tropical Atlantic), where ozone mixing ratios remain nearly constant from 1950 to 5000 m.

## 3.2 Seasonal coverage

Table S6 summarizes the number of observation days per region and season. For the ship/buoy dataset, the four seasons were relatively well sampled, but the frequencies were higher for boreal or austral summer than winter for mid- and high-latitude regions (R1, 3, 7, 9, 10, and 11). For the airborne data, coverage was less in summer than in winter over the Pacific, while the opposite was true for the Atlantic. The ozonesonde dataset appeared to have relatively uniform seasonal coverage, except that frequent observations were made during September–October–November over the Antarctic (R11).

## 3.3 Ship/buoy-based median concentrations and diurnal variation patterns in individual regions (R1–R11)

Table 3 summarizes statistics of hourly data from the ship/buoy dataset (satisfying LCL72) to compare median concentrations across defined regions (R1–R11) and to investigate features of the average diurnal profile (Fig. 6). First, the number of hourly data for individual regions ranged from 3446 (R4) to 61 708 (R10), highlighting the advantage of having this large dataset in one place. For R10 (Arc-
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Latitudinal and longitudinal transect of the ship/buoy datasets after filtering for LCL ≥ 72 h.</figcaption></figure>

tic), 31549 and 7732 h of data were from the O-Buoy (autonomous, ice-tethered buoy) and MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) missions. The datasets for regions R7–R9 (Atlantic), with contributions from the DWD-MPI cruises, were larger than those for the Pacific (R1–R3). For all regions, the data are almost equally distributed over the time of day. The average diurnal profiles were calculated as follows: local time was derived for each point by adjusting the coordinated universal time (UTC) based on longitude; then, the 25th, 50th, and 75th percentiles were calculated for each hourly bin per region.

The average of the hourly medians showed variability across regions. For the northern midlatitudes, the Pacific and Atlantic were similar (32.9 and 31.6 ppb for R1 and R7, respectively). For the tropics, the Pacific (13.8 ppb, R2) was lower than the Indian Ocean (16.2 ppb, R4) and the Atlantic (20.0 ppb, R8). For the southern midlatitudes, the values for the Pacific and Indian oceans were similar (20.1 and 19.7 ppb for R3 and R5, respectively), while the Atlantic was the lowest (15.4 ppb for R9). The R9 value is even lower than that of R8 and close to that of R11 (15.9 ppb, Antarctic). R10 (26.2 ppb) was slightly lower than R1 and R7 (northern midlatitudes). While the mixing ratios over the tropics are frequently below 15 ppb (for 30%, 50%, and 59% of the observations over the Atlantic (R8), Indian (R4), and Pacific (R2) oceans, respectively), they are very rarely near zero (< 1% of observations are less than 3 ppb; Fig. 7). This is in marked contrast to the Arctic (R10), where a secondary distribution peak is found at around zero, greater mean and median ozone levels are observed, and only 18% of mixing ratios are less than 15 ppb. Roughly one-third of these are

ozone depletion events (ODEs) and 5.9% of total observations are below 3 ppb. This indicates that the mechanism(s) of Arctic ODEs is either inoperative or less efficient in the tropics.

As noted in Sect. 1, a unique feature of ozone in the marine boundary layer over the remote oceans is net photochemical loss. This must result in afternoon decreases in ozone levels. Indeed, flat diurnal patterns or daytime decreases are evident for the ship/buoy data in most regions (Fig. 6, Table 3). The diurnal profiles of the oceanic data suggest that the datasets collected are representative of the marine atmosphere. More specifically, the three tropical regions, R2, R4, and R8, show relatively large daytime decreases. The local time at which the minima were recorded was 15, 16, and 15 h for R2, R4, and R8, respectively (Table 3), while the maxima were recorded at night or in the morning. The different timings must be affected by dynamics and chemistry. The amplitude (maximum minus minimum) of the diurnal variation was 1.7, 2.6, and 2.3 ppb or 12%, 16%, and 11% of the mean concentration for R2, R4, and R8, respectively. Previous studies from ship observations (Johnson et al., 1990; Thompson et al., 1993; Dickerson et al., 1999; Watanabe et al., 2005) and from coastal site observations (Oltmans, 1981; Nagao et al., 1999; Galbally et al., 2000; Read et al., 2008; Hu et al., 2010) have focused on diurnal variation with daytime decreases and reported amplitudes of 1–7.5 ppb (7%–32% of average concentration levels). These studies are mainly from single sites/campaigns for short periods of time. Our dataset will be useful to investigate the characteristics of ozone diurnal variations more comprehensively and with statistical robustness. Contributions from various chemical pathways (e.g., HOₓ and halogen cycles) will be
<figure><img src="image_7.png" /><figcaption>Figure 5. Vertical profiles of ozone concentrations by region after filtering for LCL72.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Hourly median (thick lines) and interquartile levels (thin lines) and their diurnal variation by region: (a) R1 and R7 (Pacific and Atlantic northern midlatitudes); (b) R2, R4, and R8 (Pacific, Indian, and Atlantic low latitudes); (c) R3, R5, and R9 (Pacific, Indian, and Atlantic southern midlatitudes); and (d) R10 and R11 (polar, i.e., Arctic and Antarctic regions). The blue, green, and red line colors correspond to the Pacific, Indian, and Atlantic oceans, respectively.</figcaption></figure>

<table><caption>**Table 3.** Statistics of hourly data from the ship/buoy dataset per defined region (R1–R11).</caption><thead><tr><th>Regions</th><th>Number of hourly data (satisfying LCL72)</th><th>Maximum of hourly medians (ppb)</th><th>Local time (hour) of maximum</th><th>Minimum of hourly medians (ppb)</th><th>Local time (hour) of minimum</th><th>Average of 24 hourly medians (ppb)</th><th>Amplitude (max-min) (ppb)</th><th>Percentage amplitude (max-min)/average (%)</th></tr></thead><tbody><tr><td>R1</td><td>6572</td><td>34.3</td><td>5</td><td>31.2</td><td>20</td><td>32.9</td><td>3.1</td><td>9.4</td></tr><tr><td>R2</td><td>9708</td><td>14.6</td><td>1</td><td>12.9</td><td>15</td><td>13.8</td><td>1.7</td><td>12.3</td></tr><tr><td>R3</td><td>6432</td><td>20.7</td><td>1, 4</td><td>19.5</td><td>14</td><td>20.1</td><td>1.2</td><td>5.7</td></tr><tr><td>R4</td><td>3446</td><td>17.3</td><td>3</td><td>14.7</td><td>16</td><td>16.2</td><td>2.6</td><td>16.0</td></tr><tr><td>R5</td><td>5651</td><td>20.0</td><td>8</td><td>19.2</td><td>0</td><td>19.7</td><td>0.9</td><td>4.4</td></tr><tr><td>R7</td><td>14 777</td><td>32.5</td><td>1, 3, 4</td><td>30.5</td><td>14</td><td>31.6</td><td>2.0</td><td>6.5</td></tr><tr><td>R8</td><td>18 818</td><td>21.3</td><td>5</td><td>19.0</td><td>15</td><td>20.0</td><td>2.3</td><td>11.3</td></tr><tr><td>R9</td><td>13 710</td><td>16.0</td><td>4</td><td>15.0</td><td>15, 16</td><td>15.4</td><td>1.0</td><td>6.5</td></tr><tr><td>R10</td><td>61 708</td><td>27.5</td><td>11</td><td>25.8</td><td>8, 13</td><td>26.2</td><td>1.7</td><td>6.5</td></tr><tr><td>R11</td><td>20 215</td><td>16.0</td><td>0–7, 16–23</td><td>15.5</td><td>13</td><td>15.9</td><td>0.5</td><td>3.3</td></tr></tbody></table>

discussed by comparison with model simulations in the upcoming assessment paper. We also plan an in-depth analysis of our first observational findings, including the substantial reduction in the tropical Indian Ocean (R4), consistent with Dickerson et al. (1999), and the relatively early onset of daytime destruction for R8 and R2.

### 3.4 Consistency between the ground-based observations and the ozonesonde observations at the same sites

At six stations (Alert, Ny Ålesund, Trinidad Head, American Samoa, Syowa, and South Pole), both ground-based and ozonesonde observations were recorded. The consistency between the two datasets was checked by comparing ozone measurements at ground level and the ozonesonde
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Frequency of observed ozone concentrations in 1 ppb bins computed for ship and buoy observations with LCL ≥ 72 h for tropical regions (Pacific Ocean, R2; Indian Ocean, R4; and Atlantic Ocean, R8) contrasted with the Arctic (R10).</figcaption></figure>

data at the lowest altitude (typically around 200 m). Figure 8 shows 3-year and 2-year comparisons at Alert and American Samoa, as an example. The agreement was found for cases of episodic ozone decreases in the Arctic and for temporal patterns of variation over days and seasons at both sites, demonstrating the internal consistency of the datasets. Using the ground-based and ozonesonde observations in the Arctic (including Alert) for the year of 2015 as well as ship/buoy/aircraft observations, Gong et al. (2025) discuss the performance of two chemistry transport models. Reasonable agreement was also found with scatterplots for all six sites (Fig. S2), with $R^2$ values ranging from 0.64 to 0.95 and slopes of bivariate linear fits ranging from 0.94 to 1.11 when sonde values were plotted against surface observations made within 1 h of each other. This analysis indicated the high quality of the two datasets.

# 4 Data availability

The datasets described in this paper are available as five .csv files containing all of the corresponding metadata information. The files are named as follows:

1. toar2_oceansanst_250203.csv;

2. toar2_oceans_250203.csv;

3. toar2_oceans_250203.csv;

4. toar2_oceans_250203.csv;

5. toar2_oceans_250203.csv.

The files contain the key metadata information listed in Tables 1–5 and are available at https://doi.org/10.17596/0004044 (Kanaya et al., 2025).

<figure><img src="image_10.png" /><figcaption>**Figure 8.** Ozone concentrations from surface observations (blue) and the lowest layer of ozonesonde observations (red, ~ 200 m altitude) at (a) Alert and (b) American Samoa.</figcaption></figure>

# 5 Conclusions and outlook

Under the TOAR-II activity, the Oceans working group has, for the first time, collected and collated observational ozone data over the open oceans and polar regions on a global scale. When available, additional pollution tracers (CO, NO, $NO_2$, and CN) were also included. All of these datasets are stored in five data files classified by platform type, i.e., ship/buoy, aircraft, ozonesondes, non-polar coastal sites, and polar sites. Here, we describe the datasets and the details of the preprocessing, filtering, and flagging procedures as well as showing basic analyses of the spatiotemporal extent, diurnal variation characteristics, and internal consistency. Our focus was on the ship/buoy and aircraft observations, which contain a total of 208 291 and 424 005 records, respectively. The aircraft and ozonesonde data covered an altitude range from the surface to 5000 m, allowing a complete assessment of ozone over the oceans and polar regions with a focus on the atmospheric boundary layer (< 2000 m). All datasets were supplemented with information on the number of hours that each observed air mass was separated from land, derived from backward trajectories. The selected criterion of 72 h or more isolation from land, justified by the coincident radon observations for some selected datasets, allowed the identification of marine air masses. Flat diurnal patterns or diurnal decreases were found after air mass selection, indicating that the collected datasets are representative of the marine atmosphere. Over the tropics, the amplitude of the observed daytime decreases was 11%–16%, with the largest decrease observed in the Indian Ocean.
Although the observational data have been collected as widely as possible, they are still not sufficiently dense or homogeneous across the defined regions, particularly for the purpose of small trend detection (Chang et al., 2024). In order to interpret the data, the sampling bias needs to be assessed using atmospheric chemistry transport numerical model simulations (e.g., Sekiya et al., 2020). Despite the inhomogeneity of the observations, point-by-point comparisons with model simulations that match the space and time of the observations are useful for studying key processes and mechanisms. Seasonality and long-term trends in the oceanic and polar ozone observations will be a focus of discussion in the forthcoming assessment (Sommariva et al., 2025).

## Appendix A: Acronyms and abbreviations

<table><caption>Table A1. List of acronyms and abbreviations.</caption><thead><tr><td>Acronym</td><td>Definition</td></tr></thead><tbody><tr><td>ABLE-2B</td><td>Amazon Boundary Layer Experiment 2B</td></tr><tr><td>ABLE-3A/3B</td><td>Arctic Boundary Layer Experiment 3A/3B</td></tr><tr><td>ACCACIA</td><td>Aerosol-Cloud Coupling and Climate Interactions in the Arctic</td></tr><tr><td>ACE1</td><td>Aerosol Characterization Experiment 1</td></tr><tr><td>ACE-Asia</td><td>Aerosol Characterization Experiment - Asia</td></tr><tr><td>ACSIS</td><td>Atmospheric Chemistry and Climate of the Southern Indian Ocean</td></tr><tr><td>ACTIVATE</td><td>Aerosol Cloud meTeorology Interactions over the western Atlantic Experiment</td></tr><tr><td>AEROSOLS99-INDOEX</td><td>Indian Ocean Experiment 1999</td></tr><tr><td>ARCPAC</td><td>Aerosol, Radiation, and Cloud Processes affecting Arctic Climate</td></tr><tr><td>ARCTAS</td><td>Arctic Research of the Composition of the Troposphere from Aircraft and Satellites</td></tr><tr><td>ATOM</td><td>Atmospheric Tomography Mission</td></tr><tr><td>ATOMIC</td><td>Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign</td></tr><tr><td>BiPM</td><td>Bureau International des Poids et Mesures</td></tr><tr><td>CalNex</td><td>California Nexus</td></tr><tr><td>CAST</td><td>Coordinated Airborne Studies in the Tropics</td></tr><tr><td>CITE</td><td>Chemical Instrumentation Test and Evaluation</td></tr><tr><td>CLARIFY</td><td>Cloud-Aerosol-Radiation Interactions and Forcing: Year 2017</td></tr><tr><td>CN</td><td>Condensation Nuclei</td></tr><tr><td>CONTRAST</td><td>Convective Transport of Active Species in the Tropics</td></tr><tr><td>CSIRO</td><td>Commonwealth Scientific and Industrial Research Organisation</td></tr><tr><td>DISCOVER-AQ</td><td>Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality</td></tr><tr><td>DRAKE</td><td>Drake Passage cruise</td></tr><tr><td>DWD</td><td>Deutscher Wetterdienst</td></tr><tr><td>DYNAMO</td><td>Dynamics of the Madden-Julian Oscillation</td></tr><tr><td>EANET</td><td>Acid Deposition Monitoring Network in East Asia</td></tr><tr><td>ECC</td><td>Electrochemical concentration cell</td></tr><tr><td>FAAM</td><td>Facility for Airborne Atmospheric Measurements</td></tr><tr><td>GAW</td><td>Global Atmosphere Watch Programme</td></tr><tr><td>GRN</td><td>Global Research Network</td></tr><tr><td>HEGIFTOM</td><td>Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements</td></tr><tr><td>HIPPO</td><td>HIAPER Pole-to-Pole Observations</td></tr><tr><td>HURRICANE</td><td>Hurricane Field Campaign 2006</td></tr><tr><td>HYSPLIT</td><td>Hybrid Single-Particle Lagrangian Integrated Trajectory</td></tr><tr><td>ICEALOT</td><td>International Chemistry Experiment in the Arctic Lower Troposphere</td></tr><tr><td>IGAC</td><td>International Global Atmospheric Chemistry project</td></tr><tr><td>IIOE2</td><td>Second International Indian Ocean Expedition</td></tr><tr><td>INTA</td><td>National Institute for Aerospace Technology</td></tr><tr><td>INTEX-B</td><td>Intercontinental Chemical Transport Experiment-B</td></tr><tr><td>INTEX-NA</td><td>Intercontinental Chemical Transport Experiment-North America</td></tr><tr><td>ITCT</td><td>Intercontinental Transport and Chemical Transformation</td></tr><tr><td>ITOP</td><td>Intercontinental Transport of Ozone and Precursors</td></tr><tr><td>KORUS-AQ</td><td>Korea-United States Air Quality Study</td></tr><tr><td>LCL</td><td>Last contact with land</td></tr><tr><td>MAGE</td><td>Marine Aerosol and Gas Exchange</td></tr><tr><td>Malaspina</td><td>Malaspina Circumnavigation Expedition</td></tr><tr><td>MOSAiC</td><td>Multidisciplinary drifting Observatory for the Study of Arctic Climate</td></tr><tr><td>MPI</td><td>Max Planck Institute</td></tr><tr><td>NAAMES</td><td>North Atlantic Aerosols and Marine Ecosystems Study</td></tr></tbody></table>
<table><caption>**Table A1. Continued.**</caption><thead><tr><td>Acronym</td><td>Definition</td></tr></thead><tbody><tr><td>NARE</td><td>North Atlantic Regional Experiment</td></tr><tr><td>NCEP</td><td>National Centers for Environmental Prediction</td></tr><tr><td>NEAQS</td><td>New England Air Quality Study</td></tr><tr><td>O-Buoy</td><td>Autonomous, ice-tethered buoy</td></tr><tr><td>ODEs</td><td>Ozone Depletion Events</td></tr><tr><td>PEM-Tropics</td><td>Pacific Exploratory Mission-Tropics</td></tr><tr><td>PEM-West</td><td>Pacific Exploratory Mission-West</td></tr><tr><td>POLARCAT</td><td>Polar Study using Aircraft, Remote Sensing, Surface Measurements and Models, of Climate, Chemistry, Aerosols, and Transport</td></tr><tr><td>RITS</td><td>Research in the Tropics</td></tr><tr><td>SAGA</td><td>Surface Ocean Lower Atmosphere Study</td></tr><tr><td>SEAC4RS</td><td>Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys</td></tr><tr><td>SOE</td><td>Southern Ocean Expedition</td></tr><tr><td>TAAF</td><td>Terres australis et antarctiques françaises</td></tr><tr><td>TEXAQS</td><td>Texas Air Quality Study</td></tr><tr><td>TOAR</td><td>Tropospheric Ozone Assessment Report</td></tr><tr><td>TORERO</td><td>Tropical Ocean tRoposphere Exchange of Reactive halogen species and Oxygenated VOC</td></tr><tr><td>TRACE-A</td><td>Transport and Atmospheric Chemistry near the Equator-Atlantic</td></tr><tr><td>TRACE-P</td><td>Transport and Chemical Evolution over the Pacific</td></tr><tr><td>VOCALS</td><td>VAMOS Ocean-Cloud-Atmosphere-Land Study</td></tr><tr><td>WACS</td><td>Western Atlantic Climate Study</td></tr><tr><td>WINTERSTORMS</td><td>Winter Storms Field Campaigns</td></tr><tr><td>WOUDC</td><td>World Ozone and Ultraviolet Radiation Data Centre</td></tr><tr><td>YES-AQ</td><td>Yellow Sea-Air Quality</td></tr></tbody></table>

**Supplement.** The supplement related to this article is available online at https://doi.org/10.5194/essd-17-4901-2025-supplement.

**Author contributions.** RS, ASL, and YK designed the study and led the data collection, assisted by TKK, AMaz, JEJ, SM, IEG, AMah, GC, WG, JCGM, KR, and MR. YK, FT, YI, HT, KK, JEJ, ASL, AC, PT, SM, IEG, RV, AMah, JS, HA, BB, MDS, DH, JG, ML, SCC, and IO carried out ship observations, collected data, and contributed to their quality control. JWH and PBS led the O-Buoy observations and contributed to the quality control of these data. RV, TKK, JL, DDP, JSH, TBR, IBP, EJW, BML, AJW, TC, FMF, JRS, IB, JP, CRT, RMStae, and AAA conducted aircraft observations, collected data, and contributed to data quality control. RVM, AMT, RMStau, DEK, JCGM, and MF performed ozonesonde observations, managed the data, and contributed to data quality control and homogenization. SM, IEG, WG, KS, JK, MNC, and MGS contributed to data collection from coastal/polar sites and the analysis of these data. MGS supervised the data collection and handling. MP contributed to data collection from surface sites. KCA and GC managed data and contributed to data curation, including quality assurance. KK and TKK performed filtering of the ship-based data and figure generation. AMaz, TKK, KR, MR, IEG, RS, ASL, and YK analyzed the dataset and prepared the figures and tables. YK drafted the manuscript. All of the co-authors reviewed manuscript and contributed to revisions.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Special issue statement.** This article is part of the special issue "Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/ESSD/GMD interjournal SI)". It is a result of the Tropospheric Ozone Assessment Report, Phase II (TOAR-II, 2020-2024).

**Acknowledgements.** We acknowledge the NSF, UCAR/NCAR, NASA, NOAA (PMEL, CSD), ECCC, EANET, JAMSTEC, and NERC for funding and data management. With respect to the R/V *Mirai* cruises, we gratefully acknowledge the assistance from the principal investigators (PIs) and staff members of all cruises and the support from Global Ocean Development Inc. and Nippon Marine Enterprise, Ltd. The authors thank the IN MAP-IO support team of the OSU-R and OMP for data from the *Marion Dufresne*. Peter Winkler, Marian de Reus, Jos Lelieveld, Jonathan Williams, and Horst Fischer are acknowledged for the DWD-MPI dataset. We acknowledge the use of the CSIRO Marine National Facility (https://ror.org/01mae9353, last access: 6 September 2025) in undertaking this research on R/V *Investigator*. The finalized data will be available from the CSIRO Data Access Portal (DAP) with an accompanying DOI. Paty Matrai, Jan Bottenheim, and Bill Simpson are acknowledged for the O-Buoy data. The FAAM airborne
data were obtained using the BAe 146-301 atmospheric research aircraft (ARA) flown by Airtask Ltd and managed by the FAA Airborne Laboratory, jointly operated by UKRI and the University of Leeds. The HEGIFTOM homogenized ozonesonde data are from PIs David Tarasick, Peter von der Gathen, Nis Jepsen, Rigel Kivi, Bryan Johnson, and Terry Deshler. The WOUDC ozonesonde data are from the Finnish Meteorological Institute – National Meteorological Service of Argentina, Australian Bureau of Meteorology, and Japan Meteorological Agency (JMA) and were retrieved from the WOUDC site (https://woudc.org/, last access: 6 September 2025). The ozonesonde sounding campaign from the research vessel *Shoyo-Maru* was conducted as part of the project “Soundings of Ozone and Water in the Equatorial Region” (SOWER) in collaboration with the Japan Fisheries Agency. Regarding the data from Kennaook/Cape Grim, the Australian Bureau of Meteorology, who own and manage the station, and the CSIRO, who produced the dataset, are acknowledged. We gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport model (https://www.arl.noaa.gov/hysplit/, last access: 23 November 2024). We also acknowledge NDACC, HEGIFTOM, and SHADOZ (https://doi.org/10.57721/SHADOZ-V06) for storing the original ozonesonde data. The ozone data from the Belgrano station would not have been possible without the collaboration agreement between INTA and National Antarctic Division (DNA)/Argentinian Antarctic Institute (IAA) and the work done by the different technicians at Belgrano.

**Financial support.** This research has been supported by the Swiss Polar Institute (grant no. DIRCR-2018-004). Data collection was carried out as part of the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition with the tag MOSAiC20192020, with activities supported by the *Polarstern* expedition AWI_PS122_00. The observations during the MOSAiC expedition were funded by the US National Science Foundation (grant nos. OPP 1807496, 1914781, and 1807163), the Swiss National Science Foundation (grant no. 200021_188478), the Swiss Polar Institute (grant no. DIRCR-2018-004), the Department of Energy Atmospheric System Research Program (grant no. DE-SC0019251), and the US National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory. A subset of data were provided by the Atmospheric Radiation Measurement (ARM) user facility, a US Department of Energy Office of Science user facility managed by the Biological and Environmental Research program. Julia Schmale holds the Ingvar Kamprad chair for extreme environments research, sponsored by Ferring Pharmaceuticals. Matthew D. Shupe was supported by the DOE (grant no. DE-SC0021341) and NOAA cooperative agreement no. NA22OAR4320151. Ilana B. Pollack, Jeff Peischl, Chelsea R. Thompson, Ilann Bourgeois, and Brian M. Lerner were supported in part by NOAA cooperative agreement nos. NA17OAR4320101 and NA22OAR4320151. Detlev Helmig received funding from the US National Science Foundation, Interdisciplinary Biocomplexity in the Environment Program (project no. BE-IDEA 0410058), and from a grant from NOAA's Climate and Global Change Program (grant no. NA07OAR4310168). The TORERO (Tropical Ocean Troposphere Exchange of Reactive halogen species and Oxygenated VOC) and CONTRAST (CONvective TTransport of Active Species

in the Tropics) projects were funded by the National Science Foundation (grant nos. AGS-1104104 and AGS-1261740). The involvement of the NSF-sponsored Lower Atmospheric Observing Facilities, managed and operated by the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) is acknowledged. This study was supported by the KAKENHI (grant no. 21H04933), the ArCS (Arctic Challenge for Sustainability; grant no. JPMXD1300000000) and ArCS II (grant no. JPMXD1420318865) of the Ministry of Education, Culture, Sports, Science, and Technology of Japan, the Specified Critical Technologies Research Promotion Grants from the Cabinet Office, Government of Japan, and by the Environmental Research and Technology Development Fund (ERTDF) from the Ministry of the Environment, Japan (grant nos. JPMEERF20252001 and JPMEERF24S12200). The O-buoy studies were supported by NSF award numbers 0612457, 1023118, 1023393, 1022834, and 1022773. Marios Panayi received support from the EMME-CARE project, funded by the European Union's Horizon 2020 Research and Innovation program (grant agreement no. 856612, with co-funding from the Government of Cyprus), and to the EU project AVENGERS (grant agreement no. 101081322). Data from Belgrano station has been partially funded by INTA and the Spanish Science Agency within the framework of the project GARDENIA ("Gases and aerosols in Antarctica: distribution, context and variability", PID2021-122737NB-I00) and other former projects.

**Review statement.** This paper was edited by Luis Millan and reviewed by two anonymous referees.

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