# Evidence for Subtropical Dynamical Changes in Reunion Ozone Trends (1998-2021)

Tristan Millet, Hassan Bencherif, Nelson Bègue, Thierry Portafaix, Abdoulwahab Mohamed Toihir, Venkataraman Sivakumar, Lucas Vaz Peres, Mathieu Fontaine, Andrea Pazmiño, Jean-Marc Metzger, et al.

► To cite this version:

Tristan Millet, Hassan Bencherif, Nelson Bègue, Thierry Portafaix, Abdoulwahab Mohamed Toihir, et al. Evidence for Subtropical Dynamical Changes in Reunion Ozone Trends (1998-2021). Earth and Space Science, 2025, 12 (12), pp.e2025EA004398. 10.1029/2025EA004398. hal-05059170v2

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Distributed under a Creative Commons Attribution 4.0 International License
# Earth and Space Science

## RESEARCH ARTICLE

10.1029/2025EA004398

### Special Collection:

Scientific Impact of Long-term
Observations of the Atmosphere
- In Honor of Walter Komhyr
(1931 - 2022)

### Key Points:

* Reunion ozone trends show significant increases in total and tropospheric ozone levels

* A turning point in stratospheric ozone trends is detected around 2008 marking a transition from decreasing to increasing ozone levels

* Atmospheric changes, that is shifts in regional anticyclones, helped drive enhanced ozone transport from Africa to Reunion in 1998–2021

### Correspondence to:

T. Millet,
tristan.millet@univ-reunion.fr

### Citation:

Millet, T., Bencherif, H., Bègue, N., Portafaix, T., Toihir, A. M., Sivakumar, V., et al. (2025). Evidence for subtropical dynamical changes in Reunion ozone trends (1998–2021). *Earth and Space Science*, 12, e2025EA004398. https://doi.org/10.1029/2025EA004398

Received 18 APR 2025

Accepted 29 OCT 2025

### Author Contributions:

**Conceptualization:** Tristan Millet, Hassan Bencherif, Nelson Bègue

**Data curation:** Tristan Millet, Mathieu Fontaine

**Funding acquisition:** Hassan Bencherif, Nelson Bègue

**Investigation:** Tristan Millet

**Methodology:** Tristan Millet, Hassan Bencherif, Nelson Bègue, Lucas Vaz Peres

**Software:** Tristan Millet, Adboulwahab M. Toihir, Mathieu Fontaine

**Supervision:** Hassan Bencherif, Nelson Bègue

**Visualization:** Tristan Millet

**Writing – original draft:** Tristan Millet

© 2025. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

<figure><img src="image_2.png" /></figure>

## Evidence for Subtropical Dynamical Changes in Reunion Ozone Trends (1998–2021)

Tristan Millet¹ ID, Hassan Bencherif¹, Nelson Bègue¹, Thierry Portafaix¹,², Adboulwahab M. Toihir³, Venkataraman Sivakumar⁴, Lucas Vaz Peres⁵, Mathieu Fontaine¹, Andrea Pazmiño⁶, Jean-Marc Metzger⁷, Ryan M. Stauffer⁸ ID, Debra E. Kollonige⁸,⁹, and Anne M. Thompson⁸,¹⁰ ID

¹LACy, Laboratoire de l'Atmosphere et des Cyclones (UMR 8105) CNRS, Université de La Réunion, Météo-France, Saint-Denis de La Réunion, France, ²Institut de Recherche pour le Développement (IRD), Antananarivo, Madagascar, ³Agence Nationale de l'Aviation Civile et de la Météorologie, Moroni, Comoros, ⁴S.V.Raman Researchers Roadmap, Durban, South Africa, ⁵Institute of Engineering and Geosciences, Federal University of Western Para (UFOPA), Santarém, Brazil, ⁶LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France, ⁷Observatoire des Sciences de l'Univers de La Réunion (OSU-Réunion), Université de La Réunion, CNRS, IRD, Météo France, Saint-Denis, France, ⁸Atmospheric Chemistry and Dynamics Laboratory, NASA GSFC, Greenbelt, MD, USA, ⁹SSAI, Lanham, MD, USA, ¹⁰University of Maryland-Baltimore County, Baltimore, MD, USA

### Abstract
We provide trend estimates for total, stratospheric, and tropospheric ozone columns over Reunion (21.1°S, 55.5°E) from 1998 to 2021, using only Système d'>Analyse par Observation Zénithale and Southern Hemisphere Additional OZonesonde observations. Trends are derived using Trend-Run, a multiple linear regression model, and a dynamic linear model (DLM) to identify potential turning points. Overall, total ozone exhibits a positive trend (3.0 ± 1.5 DU/decade), with increases in both stratospheric (1.1 ± 1.6 DU/decade) and tropospheric ozone (2.2 ± 1.0 DU/decade). DLM identifies a turning point in stratospheric ozone in 2008, with a clear decrease in stratospheric ozone before this point and an increase afterward. We also determined changes in the lapse rate tropopause (LRT), the subtropical barrier position, and ERA5 wind and geopotential fields during the same period to investigate possible links between mid-tropospheric ozone increase and transport-related perturbations. Although trends in LRT height and temperature are barely significant, they suggest a recent deepening of the troposphere, indicative of climate change. Intensification of the anticyclonic gyre over Southern Africa and a weakening of the Mascarene anticyclone are found. This suggests that, independent of possible changes in ozone precursor emissions over Africa or South America, dynamics are driving increases of ozone and ozone precursors over Reunion from 1998 to 2021. Furthermore, the rate of Reunion's free tropospheric trends exceeds that observed at all other southern hemisphere ozonesonde stations, including those in tropical, subtropical and mid-latitude regions.

### Plain Language Summary
This study examines long-term changes in ozone levels over Reunion (21.1°S, 55.5°E) from 1998 to 2021, using ground-based observations. We analyze trends in total, stratospheric, and tropospheric ozone and explore the atmospheric processes influencing these trends. Our findings indicate that total and stratospheric ozone levels exhibit positive trends, while tropospheric ozone is increasing significantly. This increase appears linked to shifts in atmospheric circulation patterns, leading to enhanced transport of pollution from Southern Africa. A change in the stratospheric ozone trend was detected around 2008, with declining levels before this point and increasing levels afterward. Additionally, we observe subtle changes in the height of the tropopause, possibly linked to climate change.

## 1. Introduction

Ozone plays a crucial role in Earth's atmosphere and has a maximum concentration in the stratosphere. Stratospheric ozone is the primary molecule responsible for absorbing solar UV-C radiation (100–280 nm), most UV-B radiation (280–315 nm), and a smaller fraction of UV-A radiation (315–400 nm) (Dahlback, 2008; Orphal et al., 2016). As a result, stratospheric ozone is vital for the biosphere by reducing harmful UV radiation at the surface, protecting ecosystems and human health. Excessive exposure to UV radiation can indeed cause severe health problems, including cataracts, melanoma, and skin aging (Matsumura & Ananthaswamy, 2004; Pitts et al., 1977). From the 1970s to the late 1990s, anthropogenic emissions of chlorofluorocarbons (CFCs) and halons significantly depleted stratospheric ozone, most notably evidenced by the Antarctic ozone hole (Farman et al., 1985; Rowland, 1996). The 1987 Montreal Protocol, which phased out CFC production, has led to a gradual
**Writing – review & editing:**

Tristan Millet, Hassan Bencherif,
Nelson Bègue, Thierry Portafaix,
Adboulwahab M. Toihir,
Venkataraman Sivakumar, Lucas
Vaz Peres, Mathieu Fontaine,
Andrea Pazmiño, Jean-Marc Metzger,
Ryan M. Stauffer, Debra E. Kollonige,
Anne M. Thompson

recovery of the ozone layer. It is projected that ozone levels will return to their 1970s state between the mid and late 21st century, depending on latitude (Dhomse et al., 2018; WMO, 2022).

Tropospheric ozone is of interest due to its role in radiative forcing as a greenhouse gas and in tropospheric chemistry as the primary source of OH radicals (IPCC, 2013; WMO, 2018). It is a major contributor to the oxidation of chemical species crucial for the radiative budget and acidification processes in the lower atmosphere (Baldy et al., 1996). Tropospheric ozone also indirectly affects radiative forcing by influencing vegetation, carbon uptake (Lombardozzi et al., 2015; Sitch et al., 2007), and methane's atmospheric lifetime (Fiore et al., 2008; West et al., 2007). It poses threats to agriculture and human health. Ozone exposure triggers defense mechanisms in crops, leading to cell death, reduced photosynthesis, and decreased yields (Avnery et al., 2011; Mills et al., 2018). Prolonged human exposure to surface ozone is associated with adverse health effects and increased mortality (Strode et al., 2019; Turner et al., 2016), contributing to millions of premature deaths worldwide (Gaudel, 2022; HEI, 2020).

Tropospheric ozone forms primarily through photochemical reactions involving the concomitant presence of solar radiation and ozone precursors, namely volatile organic compounds (VOCs) and nitrogen oxides (NOₓ) (Ivatt et al., 2022; Monks et al., 2015; Young et al., 2018). On a global basis, industrialization has greatly increased emissions of VOC, carbon monoxide (CO) and NOₓ, leading to degraded air quality. Regulation of these emissions in many countries has led to decreases in NOₓ and CO, readily seen from satellite (e.g., Duncan et al. (2016); Buchholz et al. (2021)) and to lower ozone levels in many urban centers. In more remote regions, however, NOₓ, VOC and CO emissions have a range of human and natural sources, resulting in large temporal and geographic variations in tropospheric ozone. Interactions of trace gas species with meteorological processes lead to short-term, seasonal and inter-annual variations in ozone. Surface observations, aircraft profiling, ground-based instruments (De Mazière et al., 2018) and satellites have been used to investigate tropospheric ozone variability. In the tropics, in addition to rapid urbanization (Cooper et al., 2014; Zhang et al., 2016) ozone is strongly influenced by biomass burning (Andreae & Merlet, 2001; Baldy et al., 1996; Bègue et al., 2021; Jaffe & Wigder, 2012; Randriambelo et al., 2000; Thompson et al., 1996), convective activity and lightning (Pickering et al., 1996; Thompson et al., 1997; Verma et al., 2021), stratospheric intrusions (Clain et al., 2010; Diab et al., 1996; Thompson et al., 2010) seasonal shifts of the inter-tropical convergence zone and inter-hemispheric transport (Gaudel et al., 2024; Sauvage, Martin, van Donkelaar, Liu, et al., 2007; Sauvage, Martin, van Donkelaar, & Ziemke, 2007; Thompson et al., 2012). The signatures of the Quasi-Biennial Oscillation, the El-Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole are readily seen in satellite data and profiles from ozonesondes (Randel & Thompson, 2011; Thompson et al., 2001, 2011; Ziemke & Chandra, 2003; Ziemke et al., 2006).

Monitoring global ozone levels is essential for assessing the recovery of the ozone layer and increasing tropo-spheric pollution. Studies consistently show a global decline in total ozone from the late 1970s to the mid-1990s, followed by a slower recovery, with the magnitude and turning point of trends varying by latitude (Chehade et al., 2014; Weber et al., 2018, 2022). Trends are found to have greater amplitude with increasing latitude (Toihir et al., 2018). Weber et al. (2022) reported a near-global (60°S–60°N) ozone recovery of 0.4 ± 0.2 (2σ%)/decade, reflecting gradual increase of the ozone layer due to the reduction of ozone depleting substance (ODS) emissions. Lower stratospheric ozone continues to show negative trends across all latitudes (Ball et al., 2019; Godin-Beekmann et al., 2022). In the tropics the small negative trends seen by satellite agree with analyses from ozonesondes (Thompson et al., 2021, 2025). In interpreting stratospheric ozone trends, Laine et al. (2014) challenged traditional multiple linear regression (MLR) methods by using a dynamic linear model (DLM), revealing that tropical ozone recovery began earlier than in mid-latitudes, suggesting that fixed turning points may not capture all trend features.

In contrast to total and stratospheric ozone, determining trends in tropospheric ozone globally remains a challenge even after 30 years of efforts with satellite products (Gaudel et al., 2018; Ziemke et al., 2019). One reason is that satellite estimates of tropospheric ozone typically report column amounts or mixing ratios at a limited set of pressure levels. Another factor is ensuring consistent calibration of instruments over long periods, as they degrade or get replaced. Comparisons of tropospheric ozone products from 60°N to 60°S in Gaudel et al. (2018) and Keppens et al. (2025) show how divergent their measurements are over a given area. Even over the tropics, where a relatively thin and invariant stratospheric ozone column allows for the most reliable estimate of tropospheric ozone (Thompson et al., 2021), an updated comparison by Gaudel et al. (2024) of 6 satellite products (post-2005) showed divergent and sometimes large discrepancies with profiles from ozonesondes and aircraft. As a result, in
the past several years, more effort has been put into using records of ground-based observations for determining tropospheric ozone trends. For the tropics, re-processed southern hemisphere (SH) ADditional OZonesondes (SHADOZ: Witte et al., 2017; Thompson et al., 2017; Witte et al., 2018) were used in analyses of Thompson et al. (2021, 2025) based on MLR and Gaudel et al. (2024) who used Quantile Regression (QR). Gaudel et al. (2024) also used IAGOS aircraft profiles from the equatorial Americas, Africa and southeast Asia. The years covered in Thompson et al. (2021) were 1998–2019. In Gaudel et al. (2024) sonde and IAGOS data were analyzed for 2005–2019, with 2005 chosen as the start year to align with the first full year of Aura satellite observations. Both studies reported annually averaged trends exhibiting regional variability and found the largest tropospheric trends over Southeast Asia. Gaudel et al. (2024) examined changes in the median and the 5th and 95th percentiles of ozone. In Thompson et al. (2021), updated in Thompson et al. (2025), monthly trends in free tropospheric (FT) ozone (5 km to tropopause) revealed the strongest positive trend occurred during the low-ozone season (January to April), suggesting an increase in ozone minima over past decades (Thompson et al., 2021). Notably, positive trends in the FT coincide with the annual minimum of FT ozone levels and periods of significant convection (February to March). A hypothesis that decreasing convection from 1998 to 2019 was a driver for these early-year increases was examined in detail and confirmed by Stauffer et al. (2024).

These SHADOZ and IAGOS-based studies with ozone profiles were focused on the equatorial region (within ±20° in Gaudel et al. (2024) and within ±15° in Thompson et al. (2021, 2025)). Under the auspices of the Tropospheric Ozone Assessment Report (TOAR) II, an international activity called Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements (HEGIFTOM) undertook the reprocessing of five types of ground-based ozone measurements globally: ozonesondes, IAGOS aircraft, lidar, FTIR, and Umkehr measurements from Dobson spectrometers. Median trends in the nominal Tropospheric Ozone Column (TrOC) from surface to 300 hPa, based on QR, were reported for 55 instrumented sites or airports for the period 2000–2022 (Van Malderen et al., 2025). At stations with ozonesondes, FTIR, or Umkehr measurements, many negative trends were observed, primarily in northern mid-latitudes and the Arctic over North America and Europe. In the tropics, where most data come from SHADOZ sites or IAGOS airports, trends resembled those in Thompson et al. (2021, 2025) and Gaudel et al. (2024): a few uncertain negative trends, small to moderate increases at most locations, and more pronounced increases (5%–10%/decade) in Southeast Asia.

However, 3 of the 4 subtropical SHADOZ sites in Van Malderen et al. (2025) showed larger ozone trends in the HEGIFTOM analyses than the more equatorial stations. In the case of Hanoi (21.0°N, 105.8°E), increasing urban emissions, possibly augmented by seasonal fire activity, is the likely cause. However, for the two subtropical SHADOZ stations, Irene (25.9°S, 28.2°E) and Reunion (21.1°S, 55.5°E), the case is different. At Irene, in the urban Gauteng region, near-surface ozone has declined due to air quality regulations (Thompson et al., 2025). Both Irene and Reunion are located near the subtropical jet, receiving air from both mid-latitude and tropical regions. Mostly during winter and spring, mid-latitude air masses lead to stratosphere-troposphere exchange (STE) (Clain et al., 2010; Diab et al., 1996, 2004). The southern African fire season, September-October-November (SON) coincides with the peak occurrence of STE events (Clain et al., 2010). During June-July-August (JJA), the proximity and intensification of the subtropical jet also contribute to ozone variability through STE processes (Clain et al., 2010). Background ozone levels (defined as those unaffected by pollution, stratospheric intrusions, or vertical redistribution from deep convection) are most evident during March-April-May (MAM) and convection-related events dominate during December-January-February (DJF), leading to vertical redistribution of ozone (Baldy et al., 1996; Baray et al., 1998; Clain et al., 2009; Leclair De Bellevue et al., 2006; Randriambelo et al., 2000). Note that a trends study of FT ozone profiles from 1990 to 2011 at Irene and 1992–2012 at Reunion found the strongest positive increases occurring from May through August (Clain et al., 2009; Thompson et al., 2014; Toihir et al., 2018), outside the southern African fire season. These findings motivate a revisit of the Reunion ozonesonde record.

Reunion is a subtropical pristine site in the SH, situated at the intersection of multiple atmospheric pathways. This site is regularly under the influence of air masses with ozone precursors from Madagascar, Southern Africa and South America, but studies have also shown that it is sometimes affected by advection from Australia and South-Eastern Asia (Baldy et al., 1996; Bègue et al., 2020, 2021, 2024; Bencherif, Bègue, et al., 2020; Duflot et al., 2010, 2011, 2022; Ranaivomboka et al., 2023; Randriambelo et al., 2000; Vigouroux et al., 2012). It has been shown that convection, typical over the Indian Ocean basin in austral summer, can lead to the transfer of ozone-poor air masses from the boundary layer to the upper troposphere (Leclair De Bellevue et al., 2006). The site's proximity to
the tropics means that the stratosphere above Reunion is influenced periodically by both tropical and extra-tropical air masses, with the subtropical stratospheric barrier frequently shifting over the region (Portafaix et al., 2003). As a result, stratospheric ozone at Reunion exhibits high variability due to dynamic processes such as STEs and meridional transport between the tropical stratospheric reservoir and mid-latitudes (Sivakumar et al., 2007).

The present study aims to update total, stratospheric and tropospheric column ozone trend estimates at Reunion using data from SHADOZ and a local Système d’Analyze par Observation Zénithale (SAOZ), applying both MLR and DLM approaches. In addition, we explore the relationship between the expected tropospheric ozone increases and transport-related dynamical fields. With the first ozonesondes being launched in 1992 and a SAOZ spectrometer operational since 1993, these instruments now provide over three decades of tropospheric and total ozone observations. However, to avoid natural variability introduced by major volcanic eruptions, such as Mount Pinatubo in 1991 and Hunga in 2022, and to focus on the validated SHADOZ data, the period of study is set from January 1998 to December 2021.

## 2. Instrumentation and Methodology

Ozone trends were derived from high-precision in situ observations. This section details the instruments used, the trend estimation methods, and the supplementary data sets employed to interpret these trends. Unless specified otherwise, uncertainties or standard deviations are given at the 2σ level.

### 2.1. SAOZ

The SAOZ is a ground-based passive remote sensing spectrometer operating in the visible to ultraviolet range, initially developed by the Centre National de la Recherche Scientifique (CNRS) to monitor stratospheric ozone during polar winter. It measures the sunlight scattered from the zenith sky from 300 to 650 nm (Pommereau & Goutail, 1988). Using the Differential Optical Absorption Spectroscopy technique, it measures daily ozone and nitrogen dioxide columns during sunrise and sunset with a total accuracy of 6% and 14%, respectively (Boynard et al., 2018), and a spectral resolution of ~1 nm. Integrated into the Network for the Detection of Atmospheric Composition Change (NDACC), a SAOZ instrument has been operational in Saint-Denis, Reunion, since 1993, providing over three decades of Total Column Ozone (TCO) data. Previous studies have shown good agreement between SAOZ measurements and satellite observations: SAOZ exhibits an average bias of 0.3 DU (0.1%) relative to an ensemble of satellite instruments, excluding IASI (Pastel et al., 2014), while the bias with respect to IASI at the Reunion site is 0.19% (Toihir et al., 2015). Comparisons between SAOZ TCO and TCO reconstructed from SHADOZ sonde data were performed in the context of our study (see Figure 1). With an average sonde/ SAOZ ratio of 1.0 ± 0.1, results show a good comparison between both instruments, aligning with results from Stauffer et al. (2022). The data used in this work can be downloaded either from the NDACC website (https://ndacc.larc.nasa.gov/data, last accessed on 18 September 2025) or the SAOZ website (http://saoz.obs.uvsq.fr, last accessed on 18 September 2025).

### 2.2. SHADOZ

SHADOZ was established to enhance climatological ozone profiles for satellite validation and to improve understanding of fine-scale vertical atmospheric structures and dynamics, especially in the SH (Thompson, Witte, McPeters, et al., 2003; Thompson, Witte, Oltmans, et al., 2003). Since January 1998, Reunion has been part of the SHADOZ program, with weekly sonde launches conducted to monitor ozone profiles (Sterling et al., 2018; Thompson et al., 2017; Witte et al., 2017, 2018). The present study uses paired radiosonde and ozonesonde measurements launched at Reunion airport by the Observatoire de Physique de l'Atmosphère de la Réunion (OPAR) to obtain ozone and temperature profiles. Like most SHADOZ sites, Reunion remains relatively unaffected by local urban pollution, allowing tropospheric ozone variations to reflect changes in background ozone levels (Thompson et al., 2021). Variations in ozone levels at Reunion are likely resulting from STE events, the transport from regions with biomass burning activities or the general increase in global ozone pollution. Each sonde includes an electrochemical concentration cell (ECC) to measure ozone by oxidizing a potassium iodide (KI) solution, alongside sensors for temperature, humidity, GPS, and wind speed (Komhyr, 1969; Komhyr & Harris, 1971).
It is important to note that the Reunion site used multiple temperature sondes over time. For temperature measurements, Vaisala RS80 sondes were used from September 1992 to October 2006, followed by Modem M2K2 sondes until March 2008, when the use of Modem M2K2-DC sondes commenced. Since March 2013, only Modem M10 sondes have been utilized. Comparisons between Vaisala RS80 and Modem M2K2-DC sondes were performed, indicating very good agreement between temperature and ozone profiles from both sondes, particularly within the troposphere (Posny et al., 2010). For ozone measurements, a 0.5% buffered KI solution has been used for the complete SHADOZ period. The ozonesondes used at Reunion are of the EnSci ECC type; prior to SHADOZ (before January 1998) they operated with a 1% buffered KI solution, while during the 1998–2021 SHADOZ period two different sensing solutions were used. The data were homogenized by reprocessing (Witte et al., 2018) according to the recommendations of Smit et al. (2012) and more recently Tarasick et al. (2021), Smit et al. (2021). Stauffer et al. (2022), carrying out a data quality check for ~60 global stations, found that the Reunion ozonesonde time series from 1998 to 2020 was within ±2% of independent total ozone measurements from 4 satellites instruments, with a ±5% or better agreement for stratospheric profiles compared to Aura's MLS instrument. Furthermore, data quality is ensured by the station's participation in the Jülich OzoneSonde Intercomparison Experiment (JOSIE), which standardizes preparation protocols across SHADOZ sites (Smit et al., 2007, 2012; Thompson et al., 2019). This program regrouped the principal investigators of all SHADOZ stations to assess the influence of the sonde preparations and the choice of solution on data quality. The JOSIE-2017 campaign confirmed the high quality of the SHADOZ data, achieving precision better than ±(3–5)% and an accuracy of approximately ±(5–10)% up to 30 km altitude, and provides 5% accuracy and precision in TCO since 2000 (Smit et al., 2007; Thompson et al., 2019).

For the purpose of this study, all of the ozonesonde profiles collected at Reunion in the period from January 1998 to December 2021 were processed. These were obtained from the SHADOZ website (https://tropo.gsfc.nasa.gov/shadoz/, last accessed on 18 September 2025). We excluded data before January 1998 due to the influence of the 1991 Mount Pinatubo eruption, which notably impacted global ozone and temperature patterns (Bègue et al., 2010; Mccormick et al., 1995). Indeed, Bègue et al. (2010) noted that trend results over Reunion may be biased if the influence of the Mount Pinatubo eruption was not taken into account. Similarly, to avoid perturbations from the Hunga eruption in January 2022, whose volcanic plumes directly impacted ozone levels at Reunion (Evan et al., 2023; Millet et al., 2025), our study stops in December 2021. As such, here we only take into consideration ozonesonde data obtained from January 1998 to December 2021, amounting to 24 years of high-quality data.

During this time period, a total of 777 profiles were obtained. Nevertheless, we excluded radiosoundings that did not reach the tropopause height as they prevented us from obtaining an estimation of the Tropospheric Column Ozone (TrCO). We also excluded extreme STE events that injected unusually high ozone concentrations into the troposphere, causing clear outliers. All the remaining profiles were interpolated on a vertical grid with a 100 m resolution, and all profiles within a month were averaged. Thus, a total of 759 profiles were used for this study. Following the method reported by Bencherif, Toihir, et al. (2020) and by Sivakumar et al. (2011) the TrCO was determined by integration of ozone profiles from the surface to the lapse rate tropopause (LRT). The LRT is detected as the lowest height at which the temperature gradient is greater than -2 K/km and the average temperature gradient of the next 2 km also exceeds -2 K/km.

For comparisons with the SAOZ instrument, we reconstructed TCO observations from SHADOZ data following Thompson et al. (2017). SAOZ daily TCO observations are obtained by averaging daily sunrise and sunset measurements. Sonde TCO is obtained by integrating ozonesonde measurements up to the balloon burst altitude and adding the McPeters and Labow (2012) ozone climatology above that level. Ozonesondes that do not reach 15 hPa are excluded from the data set. Figure 1 presents the comparison between SHADOZ TCO and SAOZ TCO. The two data sets show good overall agreement, though a slight step change in 2006 is noticeable, consistent with findings from Stauffer et al. (2022). That study reported a -0.9% drop in SHADOZ TCO relative to OMI data, and the figure suggests a possible small decline compared to SAOZ as well. However, the comparison yields an average sonde/SAOZ ratio of 1.0 ± 0.1 and a relative mean-squared deviation of 4.4%, confirming the high quality of the SHADOZ data set and the good agreement between ozone profiles from sondes and TCO measurements from SAOZ at Reunion.
<figure><img src="image_3.png" /><figcaption>Figure 1. (a) Système d’Analyze par Observation Zénithale (SAOZ) daily Total Column Ozone (TCO) observations alongside the Southern Hemisphere Additional OZonesonde (SHADOZ) TCO time series. (b) The ratio Sonde/SAOZ comparing TCO from ozonesondes coincident with SAOZ-obtained TCO.</figcaption></figure>

## 2.3. Trend Derivation

SAOZ and SHADOZ data were used to obtain total and tropospheric ozone monthly time series, respectively, from 1998 to 2021. Assuming negligible ozone concentration above the stratosphere in the total column, Stratospheric Column Ozone (SCO) was derived by subtracting the TrCO time series (obtained from SHADOZ) from the TCO time series (obtained from SAOZ). The time series used for trend analysis were de-seasonalized, obtained by subtracting the long-term monthly means from the original time series. In the following subsection we describe the trend models.

### 2.3.1. Trend-Run Regression

Trend-Run (TR) is a MLR model developed at the Laboratoire de l'Atmosphère et des Cyclones (LACy), adapted from Adaptive Model UNambiguous Trend Survey, which was initially developed to study temperature trends in the northern hemisphere mid-latitudes (Hauchecorne et al., 1991; Keckhut et al., 1995). This tool has been successfully adapted and applied in a variety of trend studies in the SH (Bègue et al., 2010; Bencherif et al., 2006; Bencherif, Toihir, et al., 2020; Loua et al., 2020; Sivakumar et al., 2011; Toihir et al., 2018). The details of this model are explained in these references and a short summary is given hereafter. The model requires prior knowledge of the main forcings influencing the signal, along with appropriate proxies to quantify their evolution. Using a least-squares regression approach, TR decomposes the observed signal into contributions from each forcing, with the remaining variability treated as the residual. Ultimately, the model estimates the linear trend within the time series. In the present study, the chosen forcings are represented by the Quasi-Biennial Oscillation (QBO), the Dipole Mode Index (DMI), the ENSO and the 11-year solar cycle. Two QBO proxies are used, corresponding to two orthogonal components calculated with principal component analysis, QBO₁ and QBO₂. These were computed using all QBO indexes from https://acd-ext.gsfc.nasa.gov/Data_services/met/qbo/QBO_Singapore_Uvals_GSFC.txt. The ENSO proxy is the Multivariate ENSO Index (MEI), acquired from https://psl.noaa.gov/enso/mei/data/meiv2.data and that of the solar cycle is the solar radio flux at 10.7 cm (https://psl.noaa.gov/data/correlation/solar.data). The DMI index we used corresponds to the standard DMI index from https://psl.noaa.gov/data/timeseries/month/data/dmi.had.long.data. The variability of the time series is therefore reproduced by TR following the equation:

$$A(t) = a_1 \times QBO_1(t) + a_2 \times QBO_2(t) + a_3 \times DMI(t) + a_4 \times ENSO(t) + a_5 \times Solar(t) + \epsilon(t), \quad (1)$$

where A(t) is the original time series, the coefficients $a_k$ are the regression coefficients and the $\epsilon(t)$ term represents the residual term, assumed to consist of the trend and noise. In this work, TR was applied to both the complete
de-seasonalized time series and the seasonal averages of the de-seasonalized data. The correlations between the predictors are very small (reaching a maximum of 0.28 between the MEI and the DMI), indicating the explanatory variables are independent. The trend uncertainty is calculated by incorporating the auto-correlation coefficient (Logan, 1994). However, due to its inherently simple design, the MLR approach is unable to identify turning points in the trend. To address this limitation, we also employed a DLM.

### 2.3.2. Dynamical Linear Model

Similar to TR, the DLM reconstructs time series variability by incorporating multiple forcings. In both models, we used identical proxies to ensure a fair comparison between the methods. Unlike TR, however, the DLM allows the regression coefficients and the trend to vary over time, providing a dynamic perspective. The key contribution of the DLM is identifying the turning point in trend changes. The functionality of the DLM used in this study is described in detail by Laine et al. (2014) and Laine (2020), and a brief summary is provided hereafter.

The DLM represents a time series in state space using the following observation and state equations:

$$y_t = F_t x_t + v_t, \quad v_t \sim N_p(0, V_t), \quad (2)$$

$$x_t = G_t x_{t-1} + w_t, \quad w_t \sim N_q(0, W_t), \quad (3)$$

where $y_t$ contains the observations and $x_t$ contains the unobserved states of the system at time $t$ (Laine et al., 2014). The matrices $F_t$ and $G_t$ represent the observation operator and model evolution operators, respectively. The elements of these matrices are automatically constructed by the algorithm based on assumptions about the relationships between observations and forcings. The terms $v_t$ and $w_t$ are Gaussian noise terms for observation and model uncertainty, with covariances $V_t$ and $W_t$. $N_p(0, V_t)$ stands for p-dimensional Gaussian distributions, with vector of zeros as mean and $V_t$ as the $p \times p$ covariance matrix (Laine et al., 2014). Given the expressions of $y_t$, $F_t$, $G_t$ and $V_t$, the DLM algorithm estimates $x_t$ and $W_t$. Estimation of $x_t$ is performed recursively using Kalman filtering, while $W_t$ is estimated via a Markov Chain Monte-Carlo (MCMC) method. Specifically, an adaptive version of the random walk Metropolis-Hastings algorithm is used to sample from the posterior distribution (Haario et al., 2006; Laine et al., 2014). With a sufficient number of simulations, the MCMC algorithm converges, providing an accurate representation of the posterior distribution, which captures the model's trend and associated uncertainties.

The acquisition of non-linear trends enables the identification of turning points, that is specific moments when the trend shifts direction. These turning points likely indicate significant changes in underlying factors or conditions. Thus, the DLM can be applied to analyze trend variations over time. In this study, we focused on examining trend changes in 10-year intervals (Laine et al., 2014). This 10-year change in the trends is computed for each time $t$ as $\Delta_{trend}(t) = \mu_{t+60} - \mu_{t-60}$, where $\mu$ represents the local trend value and time unit is expressed in month. This computation is applied consecutively to each sample realization, producing what we refer to as an annual trend change. To express this as a relative change from year to year, the result is scaled by the mean value of the original, non-deseasonalized time series. Since the annual trend change requires data points 60 months before and after a given time $t$, the resulting annual trend change is only available for a subset of the original time series. For a time series spanning 1998 to 2021, annual trend changes can be computed only between 2003 and 2017.

### 2.4. ERA5 Fields

To support the interpretation of trend results, we utilized supplementary data from ERA5 reanalyzes. This subsection outlines the data sources and methodology applied.

Tropospheric ozone has a wide range of precursor sources and exhibits high spatial and temporal variability, making the trend interpretation complex. To assess potential changes in ozone precursor sources over the study period, we downloaded ERA5 wind and geopotential maps. Specifically, we downloaded instantaneous $u$, $v$ and geopotential fields at 12:00 UTC time every 4 days from 1 January 1998, to 31 December 2021, at the 850, 550, 300, and 125 hPa pressure levels. Following the approach of Oltmans et al. (2004), these pressure levels were selected to approximately correspond to altitudes of 1.5, 5, 10, and 15 km, respectively. Data were collected over a region spanning 100°W to 100°E and from 45°S to 0° at a 0.25° × 0.25° resolution to account for the large number of potential ozone sources impacting Reunion. To assess a potential gradual shift in large-scale tropospheric circulation, we divided the ERA5 time series into two equal-length sub-parts, calculated the average wind and
<figure><img src="image_4.png" /><figcaption>**Figure 2.** De-seasonalized column ozone (blue), linear (black) and non-linear (green) trends observed in (a) Total Column Ozone (TCO), (c) Stratospheric Column Ozone (SCO) and (e) Tropospheric Column Ozone (TrCO) with the enclosing uncertainty range (shaded region). Panels (b), (d), and (f) present annual trend changes related to de-seasonalized TCO, SCO, and TrCO, respectively. TCO is derived from Système d'>Analyse par Observation Zénithale measurements, TrCO from Southern Hemisphere Additional OZonesonde ozonesondes, and SCO by subtracting TrCO from TCO.</figcaption></figure>

geopotential maps for each, and then computed the difference between these averages. This difference outlines changes in tropospheric flow, aiding in the evaluation of shifts in ozone precursor sources or variations in the intensity of circulation between Reunion and these sources over time.

Stratospheric ozone at Reunion is influenced by the seasonally varying position of the subtropical barrier (Portafaix et al., 2003), which exposes the site to both tropical and subtropical air masses. To explore potential long-term shifts in the location of the subtropical barrier over the study period, we analyzed its position. We retrieved daily averages of ERA5 Ertel's Potential Vorticity (EPV) maps at 12:00 UTC time over the entire SH, with a 1° × 1° resolution at the 475, 530, 600, and 700 K potential temperature levels (corresponding approximately to 19.5, 22, 24, and 27 km, respectively), covering 1 January 1998, to 31 December 2021. Using the DYnamical BArrier Location algorithm, we estimated the subtropical barrier position on each EPV map. This algorithm detects dynamical barriers based on the spatial EPV gradient in equivalent latitude coordinates, as defined by Nakamura (1996), with the barrier position characterized by a local maximum of the EPV gradient (Manney et al., 2022, 2023; Nash et al., 1996). For each day and potential temperature level, when a subtropical barrier was identified, its average latitude was recorded, from which monthly means were calculated. Trend estimates for this variable outline potential shifts in the subtropical barrier location over time.

ERA5 data can be acquired on the Climate Data Store (https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview, last accessed on 18 September 2025) or using the CDSAPI service.

# 3. Results and Discussion

## 3.1. Trend Results

### 3.1.1. Total Ozone

De-seasonalized total ozone trends are presented in Figure 2a, where the black solid line indicates the linear trend from TR, enclosed within the uncertainty range. The green solid line and shaded area depict the DLM trend and its associated uncertainty. All TR results for the complete and seasonal time series are presented in Table 1. Both
<table><caption>**Table 1**

Absolute Trends and Associated Uncertainties Computed Using Trend-Run for the Complete and Seasonal Time Series</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Complete</th><th colspan="4">Seasonal</th></tr><tr><th>DJF</th><th>MAM</th><th>JJA</th><th>SON</th></tr></thead><tbody><tr><td>Ozone (DU/decade)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TCO</td><td><strong>3.0 ± 1.5</strong></td><td><strong>2.6 ± 2.0</strong></td><td><strong>4.0 ± 1.5</strong></td><td><strong>3.8 ± 1.7</strong></td><td><strong>3.0 ± 2.4</strong></td></tr><tr><td>SCO</td><td>1.1 ± 1.6</td><td>-0.3 ± 2.1</td><td>1.4 ± 1.7</td><td><strong>3.1 ± 2.6</strong></td><td>1.3 ± 3.5</td></tr><tr><td>TrCO</td><td><strong>2.2 ± 1.0</strong></td><td><strong>2.7 ± 2.0</strong></td><td><strong>3.5 ± 1.9</strong></td><td>0.9 ± 1.8</td><td><strong>1.6 ± 1.6</strong></td></tr><tr><td>LRT (km or K/decade)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Height</td><td>0.0 ± 0.0</td><td>0.0 ± 0.2</td><td>0.1 ± 0.2</td><td>0.0 ± 0.2</td><td>0.0 ± 0.2</td></tr><tr><td>Temperature</td><td>-0.2 ± 0.5</td><td>-0.1 ± 0.9</td><td>-0.4 ± 0.8</td><td>-0.2 ± 1.0</td><td>0.0 ± 0.8</td></tr><tr><td>Barrier position (deg/decade)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>475 K</td><td>0.4 ± 1.0</td><td>1.3 ± 1.4</td><td>-0.6 ± 1.5</td><td>0.2 ± 1.8</td><td>0.6 ± 2.1</td></tr><tr><td>530 K</td><td><strong>1.9 ± 1.1</strong></td><td><strong>1.8 ± 1.3</strong></td><td><strong>2.1 ± 1.8</strong></td><td>1.3 ± 1.5</td><td><strong>2.3 ± 1.6</strong></td></tr><tr><td>600 K</td><td>0.7 ± 1.0</td><td>0.4 ± 1.8</td><td>0.2 ± 1.7</td><td><strong>1.9 ± 1.7</strong></td><td>0.5 ± 1.5</td></tr><tr><td>700 K</td><td>-0.8 ± 1.3</td><td>-1.5 ± 2.3</td><td><strong>-2.6 ± 2.3</strong></td><td><strong>1.5 ± 1.3</strong></td><td>0.5 ± 1.1</td></tr></tbody></table>

Note. Bold values indicate statistical significance at the 2σ level.

trend estimates show an increase in total ozone, with TR indicating a value of 3.0 ± 1.5 DU/decade (1.2 ± 0.6%/ decade). All total ozone seasonal TR trends, shown in Figure 3, are also significant and positive at the 2σ level.

The DLM annual trend change in total ozone is displayed in Figure 2b, where the shaded area shows the uncertainty around the mean. Table 2 provides the initial value, final value, and turning point of the annual trend change for the different time series. Total ozone's annual trend change initially remains close to 0, but shifts to positive and significant values, strongly suggesting increase in total ozone at Reunion in the past decade. Both total ozone trend estimates are positive and significant, thus indicating gradual total ozone increases at the study site, a consequence of the phasing out of ODS emissions in the 1990s. Recent results from Weber et al. (2022) showed tropical total ozone increase after 1996. Their results indicate a 0.2 ± 0.3%/decade trend for the 1996–2020 time period for the tropical belt (20°N–20°S). Our results, despite showing trends of greater magnitude, align with theirs in showing total ozone increasing close to the tropical belt.

A noticeable feature in the TCO time series (Figure 2a) is the cluster of large negative anomalies in 2006, peaking at -16.9 DU in August. The three consecutive TCO values from August to October 2006 represent the largest anomalies in the 24-year SAOZ record, attributable to specific dynamic and photochemical factors. A key factor in this large anomaly is the QBO, which transitioned from a westerly to an easterly phase in August 2006. During this period, ozone QBO anomalies in the 25°S–30°S band near our observation site approached -15 DU (see

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Seasonal trends and 2σ uncertainty bars for Total Column Ozone (blue), Stratospheric Column Ozone (orange) and Tropospheric Column Ozone (green) time series.</figcaption></figure>
<caption>**Table 2**</caption>

Initial and Final Values of the Annual Trend Change Estimated Using the DLM Method, Along With the Date of the Turning Point When Applicable, for All Complete Time Series

<table><thead><tr><th></th><th>Initial value</th><th>Turning point</th><th>Final value</th></tr></thead><tbody><tr><td>Ozone</td><td></td><td></td><td></td></tr><tr><td>TCO</td><td>-0.02 ± 0.09</td><td>2004–2008</td><td><strong>0.16 ± 0.11</strong></td></tr><tr><td>SCO</td><td>-0.10 ± 0.10</td><td>2008–2006</td><td><strong>0.16 ± 0.13</strong></td></tr><tr><td>TrCO</td><td><strong>0.73 ± 0.35</strong></td><td>Ø</td><td>0.31 ± 0.54</td></tr><tr><td>LRT</td><td></td><td></td><td></td></tr><tr><td>Height</td><td>-0.03 ± 0.08</td><td>2009–2004</td><td>0.06 ± 0.13</td></tr><tr><td>Temperature</td><td>0.03 ± 0.04</td><td>2009–2006</td><td>-0.02 ± 0.04</td></tr><tr><td>Barrier position</td><td></td><td></td><td></td></tr><tr><td>475 K</td><td>0.33 ± 0.41</td><td>2016–2005</td><td>-0.01 ± 0.61</td></tr><tr><td>530 K</td><td><strong>0.88 ± 0.47</strong></td><td>Ø</td><td>0.54 ± 0.63</td></tr><tr><td>600 K</td><td><strong>0.76 ± 0.53</strong></td><td>2014–2005</td><td>-0.10 ± 0.71</td></tr><tr><td>700 K</td><td><strong>-0.90 ± 0.64</strong></td><td>2013–2005</td><td>0.06 ± 0.78</td></tr></tbody></table>

Note. Bold values indicate statistical significance at the 2σ level. Turning point dates within the trends are given in YYYY-MM format.

Figure 1 in Gabis (2023)), suggesting a significant QBO influence on the TCO anomaly. A similarly large ozone QBO anomaly also impacted the previous negative TCO anomaly in 2004 (Gabis, 2023). Additionally, the solar cycle was nearing its minimum, likely contributing to lower stratospheric ozone levels (Soukharev & Hood, 2006; Toihir et al., 2018; Zerefos et al., 1997), adding to the QBO-related anomaly. Furthermore, a brief warm phase in the MEI was observed during this period. In the region, a positive MEI phase is associated with lesser ozone levels due to the enhancement of tropical convection (Toihir et al., 2018). Thus, exceptionally high negative anomalies in 2006 are attributable primarily to the QBO phase, with additional contributions from minimal solar activity and the ENSO phase.

### 3.1.2. Tropospheric Ozone

Tropospheric ozone trends are shown in Figure 2e. Both trend estimates are close to one another. TR indicates significant tropospheric ozone increase with 2.2 ± 1.0 DU/decade (6.9 ± 3.2%/decade). TR seasonal trends are significant and positive at the 2σ level for DJF, MAM and SON, and significant and positive at the 1σ level for JJA, indicating a general tropospheric ozone increase throughout the year. This suggests a gradual rise in background ozone levels, consistent with the findings of Thompson et al. (2025) who found nearly the same changes for tropospheric column ozone over 1998–2023: 2.63 ± 0.78 ppbv/decade or 7.22 ± 2.14%/decade. In Thompson

et al. (2025), annual mean increases in the tropospheric columns to LRT are determined whereas Van Malderen et al. (2025) determined tropospheric column trends only to 300 hPa. In both those studies, Reunion has the largest tropospheric ozone trend of all SH SHADOZ stations. The Reunion trend is also greater than the Lauder, New Zealand, mid-latitude tropospheric ozone increase (Van Malderen et al., 2025), making it the largest SH trend. Unlike total ozone, however, the annual trend change in tropospheric ozone (Figure 2f) displays a decline over time, beginning at 0.73 ± 0.35%/yr in 2003 and decreasing to 0.31 ± 0.54%/yr in 2017. This decrease in the tropospheric ozone annual trend change is indicative of a stabilization of de-seasonalized tropospheric ozone in the later years, although the trend remains positive and increasing. Both trend estimates align with previous research results in showing this tropospheric ozone increase. Notably, Thompson et al. (2014) and Gaudel et al. (2018) indicated particular increase in FT ozone at Reunion during winter-time.

A noticeable feature in the TrCO time series includes the early period of positive anomalies between 2004 and 2006, the largest being 9.7 DU in December 2005. These anomalies are likely associated with a specific dynamical context. Figure 4 illustrates this dynamical context by showing the difference between the 2004–2006 and the global 1998–2021 average wind maps, highlighting wind anomalies relative to the 1998–2021 period. Arrows indicate the direction and magnitude of wind speed differences, while contours represent geopotential differences, with solid lines for positive and dashed lines for negative values. These wind anomaly maps reveal transport pathways at 550 and 300 hPa, linking Reunion to major biomass burning regions, namely Southern Africa and South America. The TrCO anomalies observed in November–December 2004 and December 2005 align with these wind patterns, which likely facilitated the transport of biomass burning ozone precursors, explaining the elevated ozone anomalies during this period. In contrast, the largest negative TrCO anomalies later in the time series result from the presence of an ozone sink near the marine boundary layer and from FT air masses influenced by regions far from biomass burning areas. As an example, Figure 5 displays wind anomaly maps for December 2012, when the largest negative TrCO anomaly (–14.7 DU) is observed. In this case, Reunion is impacted at all pressure levels by air masses with a marine origin, primarily arriving from the southeast.

### 3.1.3. Stratospheric Ozone

Stratospheric ozone trends are presented in Figure 2c. SCO trend estimates from TR and DLM show different results. TR indicates a stratospheric ozone increase, though not significant at the 2σ level with 1.1 ± 1.6 DU/decade (0.5 ± 0.8%/decade). TR seasonal trends are significant and positive at the 2σ level in JJA, significant and positive at the 1σ level in MAM and non-significant in DJF and SON. The DLM trend, on the other hand, indicates a clear stratospheric ozone decrease prior to 2008 followed by a clear increase. This change is best seen in
<figure><img src="image_6.png" /><figcaption>**Figure 4.** ERA5 wind (arrows) and geopotential (contours) anomaly fields displaying the difference between the 1998–2021 and the 2004–2006 averages at the (a) 125, (b) 300, (c) 550, and (d) 850 hPa pressure levels. Solid and dashed contour lines indicate positive and negative values, respectively. The black star marks the location of Reunion. Geopotential contours are expressed in m$^2$s$^{-2}$.</figcaption></figure>

Figure 2d, where the annual trend change in stratospheric ozone changes sign in 2008, indicating this year as a turning point within the trend. The annual trend change in stratospheric ozone appears to have shifted from −0.10 ± 0.10%/yr in 2003 to +0.16 ± 0.13%/yr in 2017.

The combined SAOZ and SHADOZ data, showing large negative anomalies in total ozone and positive anomalies in tropospheric ozone around 2004–2006, contribute to the prominent negative stratospheric ozone anomalies observed in SCO during this period. This pattern results from a combination of dynamical factors (negative ozone QBO anomaly in the stratosphere and increased influence of ozone precursors in the troposphere, see Sections 3.1.1 and 3.1.2) leading to a gradual stratospheric ozone decrease from 1998 to 2006. From 2007 onward, SCO values show a slow increase, consistent with the positive TCO trend (Figure 2a). The DLM successfully identifies the turning point implied by this behavior, while TR does not obtain a significant trend at the $2\sigma$ level. However, it must be noted that TR obtains significant results for the 1998–2006 (−6.5 ± 6.0 DU/decade or −2.8 ± 2.6%/decade) and the 2007–2021 (2.6 ± 2.4 DU/decade or 1.1 ± 1.0%/decade) time periods, despite fewer data points. This further supports the existence of a turning point in the year 2008.
<figure><img src="image_7.png" /><figcaption>**Figure 5.** ERA5 wind (arrows) and geopotential (contours) anomaly fields showing the difference between the December average for 1998–2021 and the December average for 2012 at the (a) 125, (b) 300, (c) 550, and (d) 850 hPa pressure levels. Solid and dashed contour lines indicate positive and negative values, respectively. The black star marks the location of Reunion. Geopotential contours are expressed in $m^2s^{-2}$.</figcaption></figure>

Thus, SCO over Reunion is increasing, despite a period of low anomalies in 2004–2006. The DLM and segmented TR regressions agree in the existence of a turning point close to 2008. Overall, results indicate gradual stratospheric ozone increase for the entire study period and a significant and positive trend since 2008.

### 3.1.4. Tropopause

Because LRT height reflects tropospheric depth, a gradual change in LRT height or temperature may signal a corresponding change in tropospheric depth and, consequently, its ozone content. To investigate this matter, we derived LRT temperature and height trends, shown in Figure 6. Due to the large variability in both time series, TR results are non-significant, even at the $1\sigma$ level, except during MAM, with $-1.7 \pm 1.6$ (1$\sigma$) K/decade ($-0.9 \pm 0.8\%$/decade) and $0.5 \pm 0.4$ (1$\sigma$) km/decade ($3.3 \pm 2.5\%$/decade). Dynamic linear model trends are more informative as they point to significant changes visible in Figures 6b and 6d. Annual trend changes in LRT height and temperature appear to change sign simultaneously in 2009, indicating a reversal of the trend. This finding, although barely significant, is a hint toward a small-magnitude deepening of the troposphere over the study site and could partly support the observed tropospheric ozone increase. This finding is consistent with studies
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Same as Figure 2, but concerning de-seasonalized lapse rate tropopause (a, b) height and (c, d) temperature.</figcaption></figure>

indicating tropopause height increase and tropopause temperature decrease in the SH tropics and at Lauder, New Zealand (Zeng et al., 2024; Zou et al., 2023). This deepening of the troposphere is consistent with the reported poleward shift and widening of the Hadley cell, driven by anthropogenic forcing such as increased greenhouse gas concentrations (Lu et al., 2007) and stratospheric ozone depletion (Kim et al., 2017). These trends suggest a gradual shift in the LRT, potentially signaling a change in upper tropospheric dynamics that needs further investigation. The cold point tropopause (CPT) is another commonly used method to determine the height of the tropopause. Although CPT trends were also calculated for completeness (not shown), they do not indicate any significant change over time.

## 3.2. Additional Observations

### 3.2.1. Tropospheric Ozone

Tropospheric ozone variability is complex due to a large ensemble of precursor sources. To better interpret tropospheric ozone trends, we have used ERA5 data to correlate trends to potential changes in ozone sources over time.

The difference in wind and geopotential between the second and the first halves of the 1998–2021 period is shown in Figure 7 for each pressure level. Maps from 850 hPa to 300 hPa consistently show a weakening of the Mascarene anticyclone, as evidenced by decreasing geopotential and increasing clockwise wind speeds. This weakening leads to an intensified tropospheric circulation between Southern Africa and Reunion. This is clearly seen at the 300 hPa level where winds form a loop along the $150 \text{ m}^2\text{s}^{-2}$ iso-geopotential, strengthening the anticyclone gyre that transports air masses containing ozone precursors to the vicinity of Reunion (Clain et al., 2009; Diab et al., 2004). Positive geopotential and increased wind speed in the upper troposphere at the 125 hPa level further indicate an intensification of tropospheric flow from Southern Africa to the vicinity of southern Madagascar and Reunion.

Thus, the ERA5 wind and geopotential fields indicate an intensified flow and transport of ozone precursors from Southern Africa to Reunion. The rise in atmospheric pollution, as evidenced by Thompson et al. (2014), and the enhanced transport from Southern Africa align with the observed gradual increase in tropospheric ozone at Reunion. Consequently, the TrCO increase at Reunion is primarily linked to an intensifying dynamical connection between Southern Africa and Reunion. However, as shown in Figure 4, episodic transport from South America can still occur.
<figure><img src="image_9.png" /><figcaption>**Figure 7.** ERA5 wind (arrows) and geopotential (contours) fields displaying the difference between the second and the first halves of the 1998–2021 period at the (a) 125, (b) 300, (c) 550, and (d) 850 hPa pressure levels. Solid and dashed contour lines indicate positive and negative values, respectively. The black star marks the location of Reunion. Geopotential contours are expressed in $m^2s^{-2}$.</figcaption></figure>

### 3.2.2. Stratospheric Ozone

As tropospheric ozone at Reunion is linked to biomass burning, stratospheric ozone at this location is tightly influenced by the subtropical barrier (Portafaix et al., 2003). Here we describe the evolution of this barrier's position over time.

Trends of de-seasonalized subtropical barrier position at four different potential temperature levels are shown in Figure 8. TR indicates non-significant trends at 475 K, while it finds significant trends at the $2\sigma$ level at 530 K ($1.9 \pm 1.1$ deg/decade or $6.6 \pm 3.8\%$/decade) and significant trends at the $1\sigma$ level at 600 K ($0.7 \pm 1.0$ deg/decade or $2.5 \pm 3.8\%$/decade) and 700 K ($-0.8 \pm 1.3$ deg/decade or $-3.2 \pm 5.2\%$/decade). At isentropic levels below 600 K, trends are positive, suggesting an equatorward shift of the subtropical barrier, while the trend at 700 K is negative, indicating a poleward shift. The DLM supports these findings, showing increasingly significant annual trend changes with altitude. Similarly, the DLM trends indicate no significant change at 475 K, a marked equatorward shift at 530 K, an apparent equatorward drift at 600 K, and a clear poleward shift at 700 K. Annual trend changes at the 530 K level suggest a decreasing annual trend change that stays positive at the $2\sigma$ level until
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Same as Figures 2 and 6, but concerning de-seasonalized subtropical barrier position at the (a, b) 475, (c, d) 530, (e, f) 600 and (g, h) 700 K potential temperature levels.</figcaption></figure>

2016. At the 600 K level, the annual trend change decreases from $0.76 \pm 0.53$ in 2003 to $-0.10 \pm 0.71$ in 2017, while at the 700 K level increases from $-0.90 \pm 0.64$ in 2003 to $0.06 \pm 0.78$ in 2017.

According to Portafaix et al. (2003), the stratospheric ozone maximum over Reunion occurs at potential temperature levels between 600 and 700 K, with a peak near 625 K. Consequently, the arrival at Reunion of air masses originating from higher latitudes induces a positive ozone anomaly if the transport occurs below Reunion's ozone maximum, and a negative anomaly otherwise (Figure 2 of Portafaix et al. (2003)). Our results near the ozone maximum exhibit the highest significance, indicating an equatorward shift of the subtropical barrier at the 600 K level and a poleward shift at the 700 K level. Although accurately evaluating the implications of these changes on Reunion SCO is a complex task that needs further investigation, this result implies an SCO increase.

4. Concluding Remarks

Updated trends for total, stratospheric, and tropospheric ozone columns over Reunion were derived from local SAOZ and SHADOZ observations from 1998 to 2021, using both MLR and DLM methods to provide classic and dynamic trend perspectives. Generally, DLM and TR trends align closely, showing significant positive trends in total and tropospheric ozone over the study period. TR indicates an increase of $3.0 \pm 1.5$ DU/decade ($1.2 \pm 0.6\%$/decade) in total ozone and an increase of $2.2 \pm 1.0$ DU/decade ($6.9 \pm 3.2\%$/decade) in tropospheric ozone. These increases are similar to those reported for roughly the same period derived from HEGIFTOM data (Van Malderen et al., 2025) and from SHADOZ (Thompson et al., 2025). Trend results suggest a tropospheric ozone increase in all seasons, providing further evidence that the rise in tropospheric ozone background levels is not confined to the tropical belt. For stratospheric ozone, the DLM captures a turning point in 2008, with a clear decline before this
point and an increase afterward. Over 1998–2021, TR indicates an increase of 1.1 ± 1.6 DU/decade (0.5 ± 0.8%/ decade) in stratospheric ozone.

Trends in LRT height, temperature, and subtropical barrier location were also analyzed. Though barely significant at the 2σ level with the DLM, annual trend changes in LRT height and temperature suggest a turning point in 2008, with LRT height decreasing and temperature increasing thereafter, indicating a modest increase in tropospheric depth. TR obtains significant trends only in MAM at the 1σ level, with −1.7 ± 1.6 (1σ) K/decade (−0.9 ± 0.8%/decade) and 0.5 ± 0.4 (1σ) km/decade (3.3 ± 2.5%/decade). The subtropical barrier position's shift over time is altitude-dependent. Results show clear equatoward (TR: 1.9 ± 1.1 deg/decade or 6.6 ± 3.8%/decade) and poleward (TR: −0.8 ± 1.3 deg/decade or −3.2 ± 5.2%/decade) shifts of the barrier at the 530 and 700 K levels, respectively, suggesting an increase in stratospheric ozone.

ERA5 wind and geopotential maps at 300 hPa suggest an intensification of the anticyclonic gyre over Southern Africa and a weakening of the Mascarene anticyclone, resulting in increased dynamical connection between Southern Africa and Reunion.

Overall, trends in all three ozone columns at Reunion indicate a positive trajectory: (a) a clear tropospheric ozone increase, despite Reunion's location in a relatively pristine environment, and (b) signs of increase in total and stratospheric ozone. However, further investigation is needed to better assess the impact of tropospheric widening and subtropical barrier shifts on ozone levels in the troposphere and stratosphere. Additionally, the significant impact of convection on seasonal trends, as discussed in Thompson et al. (2021) and Stauffer et al. (2024), was not addressed in this study. Future research needs to explore whether convection significantly influences the observed seasonal trends at Reunion. Nonetheless, we have found that dynamical and transport changes over the 1998– 2021 period have marked impact on both tropospheric and stratospheric ozone trends at Reunion.

## Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

## Data Availability Statement

SAOZ data from the Reunion station were obtained from the Network for the Detection of Atmospheric Composition Change (NDACC) and LATMOS/UVSQ (2025). SHADOZ data were obtained from the Southern Hemisphere Additional O Zonesondes (SHADOZ) (2023). ERA5 data are available at Hersbach et al. (2023).

## References

Andreae, M. O., & Merlet, P. (2001). Emission of trace gases and aerosols from biomass burning. *Global Biogeochemical Cycles*, *15*(4), 955–966. https://doi.org/10.1029/2000GB001382

Avnery, S., Mauzerall, D. L., Liu, J., & Horowitz, L. W. (2011). Global crop yield reductions due to surface ozone exposure: 2. Year 2030 potential crop production losses and economic damage under two scenarios of O₃ pollution. *Atmospheric Environment*, *45*(13), 2297–2309. https://doi.org/10.1016/j.atmosenv.2011.01.002

Baldy, S., Ancellet, G., Bessafi, M., Badr, A., & Luk, D. L. S. (1996). Field observations of the vertical distribution of tropospheric ozone at the island of Reunion (southern tropics). *Journal of Geophysical Research*, *101*(D19), 23835–23849. https://doi.org/10.1029/95JD02929

Ball, W. T., Rozanov, E. V., Alsing, J., Marsh, D. R., Tummon, F., Mortlock, D. J., et al. (2019). The upper stratospheric solar cycle ozone response. *Geophysical Research Letters*, *46*(3), 1831–1841. https://doi.org/10.1029/2018GL081501

Baray, J., Ancellet, G., Taupin, F., Bessafi, M., Baldy, S., & Keckhut, P. (1998). Subtropical tropopause break as a possible stratospheric source of ozone in the tropical troposphere. *Journal of Atmospheric and Solar-Terrestrial Physics*, *60*(1), 27–36. https://doi.org/10.1016/S1364-6826(97)00116-8

Bègue, N., Baron, A., Krysztofiak, G., Berthet, G., Kloss, C., Jégou, F., et al. (2024). Evidence of A dual African and Australian biomass burning influence on the vertical distribution of aerosol and carbon monoxide over the southwest Indian Ocean basin in early 2020. *Atmospheric Chemistry and Physics*, *24*(13), 8031–8048. https://doi.org/10.5194/acp-24-8031-2024

Bègue, N., Bencherif, H., Jégou, F., Vérèmes, H., Khaykin, S., Krysztofiak, G., et al. (2021). Transport and variability of tropospheric ozone over Oceania and Southern Pacific during the 2019–20 Australian bushfires. *Remote Sensing*, *13*(16), 3092. https://doi.org/10.3390/rs13163092

Bègue, N., Bencherif, H., Sivakumar, V., Kirgis, G., Mze, N., & Leclair de Bellevue, J. (2010). Temperature variability and trends in the UT-LS over a subtropical site: Reunion (20.8°S, 55.5°E). *Atmospheric Chemistry and Physics*, *10*(17), 8563–8574. https://doi.org/10.5194/acp-10-8563-2010

Bègue, N., Shikwambana, L., Bencherif, H., Pallotta, J., Sivakumar, V., Wolfram, E., et al. (2020). Statistical analysis of the long-range transport of the 2015 Calbuco volcanic plume from ground-based and space-borne observations. *Annales Geophysicae*, *38*(2), 395–420. https://doi.org/10.5194/angeo-38-395-2020

Bencherif, H., Bègue, N., Kirsch Pinheiro, D., du Preez, D. J., Cadet, J.-M., da Silva Lopes, F. J., et al. (2020). Investigating the long-range transport of aerosol plumes following the Amazon fires (August 2019): A multi-instrumental approach from ground-based and satellite observations. *Remote Sensing*, *12*(22), 3846. https://doi.org/10.3390/rs12223846

## Acknowledgments

The authors acknowledge the CNRS-NRF IRP ARSAIO (Atmospheric Research in Southern Africa and Indian Ocean) project for supporting research activities, as well as the Conseil Régional de la Réunion and the European FEDER for the Ph.D. scholarship of Tristan Millet. The authors are grateful for the support provided by the MOUSSACA project through the federation Observatoire des Milieux Naturels et des Changements Globaux (OMNCG) of the OSU-Réunion (Observatoire des Sciences de l'Univers à La Réunion, UAR 3365). The authors thank the SHADOZ and SAOZ Principal Investigators for providing the data used in this study and for ensuring the quality of the observations. They also thank Marko Laine for his valuable explanations and support in understanding the mechanisms of the DLM. The authors acknowledge the support of OPAR (Observatoire de Physique de l'Atmosphère à la Réunion) and OSU-Réunion, funded by CNRS (INSU), Météo-France, and Université de La Réunion. Finally, the authors wish to express their deep gratitude, in memory of Françoise Posny, principal investigator of the SHADOZ data at Reunion from 1998 to 2021, whose dedication and contributions made this research possible.
Bencherif, H., Diab, R. D., Portafaix, T., Morel, B., Keckhut, P., & Moorgawa, A. (2006). Temperature climatology and trend estimates in the UTLS region as observed over a southern subtropical site, Durban, South Africa. *Atmospheric Chemistry and Physics*, *6*(12), 5121–5128.
https://doi.org/10.5194/acp-6-5121-2006

Bencherif, H., Toihir, A. M., Mbatha, N., Sivakumar, V., du Preez, D. J., Bègue, N., & Coetzee, G. (2020). Ozone variability and trend estimates from 20-Years of ground-based and satellite observations at irene station, South Africa. *Atmosphere*, *11*(11), 1216. https://doi.org/10.3390/atmos11111216

Boynard, A., Hurtmans, D., Garane, K., Goutail, F., Hadji-Lazaro, J., Koukouli, M. E., et al. (2018). Validation of the IASI FORLI/EUMETSAT ozone products using satellite (GOME-2), ground-based (Brewer–Dobson, SAOZ, FTIR) and ozonesonde measurements. *Atmospheric Measurement Techniques*, *11*(9), 5125–5152. https://doi.org/10.5194/amt-11-5125-2018

Buchholz, R. R., Worden, H. M., Park, M., Francis, G., Deeter, M. N., Edwards, D. P., et al. (2021). Air pollution trends measured from Terra: CO and AOD over industrial, fire-prone, and background regions. *Remote Sensing of Environment*, *256*, 112275. https://doi.org/10.1016/j.rse.2020.112275

Chehade, W., Weber, M., & Burrows, J. P. (2014). Total ozone trends and variability during 1979-2012 from merged data sets of various satellites. *Atmospheric Chemistry and Physics*, *14*(13), 7059–7074. https://doi.org/10.5194/acp-14-7059-2014

Clain, G., Baray, J. L., Delmas, R., Diab, R., Leclair de Bellevue, J., Keckhut, P., et al. (2009). Tropospheric ozone climatology at two Southern Hemisphere tropical/subtropical sites, (Reunion Island and Irene, South Africa) from ozonesondes, LIDAR, and in situ aircraft measurements. *Atmospheric Chemistry and Physics*, *9*(5), 1723–1734. https://doi.org/10.5194/acp-9-1723-2009

Clain, G., Baray, J.-L., Delmas, R., Keckhut, P., & Cammas, J.-P. (2010). A lagrangian approach to analyse the tropospheric ozone climatology in the tropics: Climatology of stratosphere–troposphere exchange at Reunion Island. *Atmospheric Environment*, *44*(7), 968–975. https://doi.org/10.1016/j.atmosenv.2009.08.048

Cooper, O. R., Parrish, D. D., Ziemke, J., Balashov, N. V., Cupeiro, M., Galbally, I. E., et al. (2014). Global distribution and trends of tropospheric ozone: An observation-based review. *Elementa: Science of the Anthropocene*, 2, 000029. https://doi.org/10.12952/journal.elementa.000029

Dahlback, A. (2008). Global monitoring of atmospheric ozone and solar UV radiation. *Solar Radiation and Human Health*. Retrieved from https://www.researchgate.net/publication/252292301

De Mazière, M., Thompson, A. M., Kurylo, M. J., Wild, J. D., Bernhard, G., Blumenstock, T., et al. (2018). The Network for the Detection of atmospheric Composition Change (NDACC): History, status and perspectives. *Atmospheric Chemistry and Physics*, *18*(7), 4935–4964. https://doi.org/10.5194/acp-18-4935-2018

Dhomse, S. S., Kinnison, D., Chipperfield, M. P., Salawitch, R. J., Cionni, I., Hegglin, M. I., et al. (2018). Estimates of ozone return dates from Chemistry-Climate Model Initiative simulations. *Atmospheric Chemistry and Physics*, *18*(11), 8409–8438. https://doi.org/10.5194/acp-18-8409-2018

Diab, R. D., Thompson, A. M., Mari, K., Ramsay, L., & Coetzee, G. J. R. (2004). Tropospheric ozone climatology over Irene, South Africa, from 1990 to 1994 and 1998 to 2002. *Journal of Geophysical Research*, *109*(D20), JD00479. https://doi.org/10.1029/2004JD004793

Diab, R. D., Thompson, A. M., Zunckel, M., Coetzee, G. J. R., Combrink, J., Bodeker, G. E., et al. (1996). Vertical ozone distribution over Southern Africa and adjacent oceans during SAFARI-92. *Journal of Geophysical Research*, *101*(D19), 23823–23833. https://doi.org/10.1029/96JD01267

Duflot, V., Bègue, N., Pouliquen, M.-L., Goloub, P., & Metzger, J.-M. (2022). Aerosols on the Tropical Island of La Réunion (21°S, 55°E): Assessment of Climatology, Origin of Variability and Trend. *Remote Sensing*, *14*(19), 4945. https://doi.org/10.3390/rs14194945

Duflot, V., Dils, B., Baray, J. L., de MazièRe, M., Attié, J. L., Vanhaelewyn, G., et al. (2010). Analysis of the origin of the distribution of CO in the subtropical southern Indian Ocean in 2007. *Journal of Geophysical Research (Atmospheres)*, *115*(D22), D22106. https://doi.org/10.1029/2010JD013994

Duflot, V., Royer, P., Chazette, P., Baray, J.-L., Courcoux, Y., & Delmas, R. (2011). Marine and biomass burning aerosols in the southern Indian Ocean: Retrieval of aerosol optical properties from shipborne Lidar and Sun photometer measurements. *Journal of Geophysical Research*, *116*(D18), D18208. https://doi.org/10.1029/2011JD015839

Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets, D. G., et al. (2016). A space-based, high-resolution view of notable changes in urban NOₓ pollution around the world (2005–2014). *Journal of Geophysical Research: Atmospheres*, *121*(2), 976–996. https://doi.org/10.1002/2015JD024121

Evan, S., Brioude, J., Rosenlof, K. H., Gao, R.-S., Portmann, R. W., Zhu, Y., et al. (2023). Rapid ozone depletion after humidification of the stratosphere by the Hunga Tonga Eruption. *Science*, *382*(6668), eadg2551. https://doi.org/10.1126/science.adg2551

Farman, J. C., Gardiner, B. G., & Shanklin, J. D. (1985). Large losses of total ozone in Antarctica reveal seasonal ClOₓ/NOₓ interaction. *Nature*, *315*(6016), 207–210. https://doi.org/10.1038/3152070a

Fiore, A. M., West, J. J., Horowitz, L. W., Naik, V., & Schwarzkopf, M. D. (2008). Characterizing the tropospheric ozone response to methane emission controls and the benefits to climate and air quality. *Journal of Geophysical Research*, *113*(D8), D08307. https://doi.org/10.1029/2007JD009162

Gabis, I. (2023). Quasi-biennial modulation of total ozone distribution from equator to polar regions based on observations. *Journal of Atmospheric and Solar-Terrestrial Physics*, *247*, 106076. https://doi.org/10.1016/j.jastp.2023.106076

Gaudel, A. (2022). A step forward to mitigate ozone. *Nature Geoscience*, *15*(7), 513–514. https://doi.org/10.1038/s41561-022-00975-6

Gaudel, A., Bourgeois, I., Li, M., Chang, K.-L., Ziemke, J., Sauvage, B., et al. (2024). Tropical tropospheric ozone distribution and trends from in situ and satellite data. *Atmospheric Chemistry and Physics*, *24*(17), 9975–10000. https://doi.org/10.5194/acp-24-9975-2024

Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows, J. P., et al. (2018). Tropospheric Ozone Assessment report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. *Elementa: Science of the Anthropocene*, *6*, 39. https://doi.org/10.1525/elementa.291

Godin-Beekmann, S., Azouz, N., Sofieva, V. F., Hubert, D., Petropavlovskikh, I., Effertz, P., et al. (2022). Updated trends of the stratospheric ozone vertical distribution in the 60°S-60°N latitude range based on the LOTUS regression model. *Atmospheric Chemistry and Physics*, *22*(17), 11657–11673. https://doi.org/10.5194/acp-22-11657-2022

Haario, H., Laine, M., Mira, A., & Saksman, E. (2006). DRAM: Efficient adaptive MCMC. *Statistics and Computing*, *16*(4), 339–354. https://doi.org/10.1007/s11222-006-9438-0

Hauchecorne, A., Chanin, M.-L., & Keckhut, P. (1991). Climatology and trends of the middle atmospheric temperature (33–87 km) as seen by Rayleigh lidar over the south of France. *Journal of Geophysical Research*, *96*(D8), 15297–15309. https://doi.org/10.1029/91JD01213

HEI. (2020). *State of global air 2020*. Health Effects Institute. Retrieved from https://www.stateofglobalair.org/

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Muñoz Sabater, J., et al. (2023). Era5 hourly data on pressure levels from 1940 to present [Dataset]. *Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. https://doi.org/10.24381/cds.bd0915c6
IPCC. (2013). Climate change 2013: The physical science basis. In *Contribution of working Group I to the fifth assessment report of the intergovernmental panel on climate change*. Cambridge University Press. Retrieved from https://www.ipcc.ch/report/ar5/wg1/

Ivatt, P. D., Evans, M. J., & Lewis, A. C. (2022). Suppression of surface ozone by an aerosol-inhibited photochemical ozone regime. *Nature Geoscience*, **15**(7), 536–540. https://doi.org/10.1038/s41561-022-00972-9

Jaffe, D. A., & Wigder, N. L. (2012). Ozone production from wildfires: A critical review. *Atmospheric Environment*, **51**, 1–10. https://doi.org/10.1016/j.atmosenv.2011.11.063

Keckhut, P., Hauchecorne, A., & Chanin, M. L. (1995). Midlatitude long-term variability of the middle atmosphere: Trends and cyclic and episodic changes. *Journal of Geophysical Research*, **100**(D9), 18887–18897. https://doi.org/10.1029/95JD01387

Keppens, A., Hubert, D., Granville, J., Nath, O., Lambert, J.-C., Wespes, C., et al. (2025). Harmonisation of sixteen tropospheric ozone satellite data records. In *EGUsphere* (pp. 1–38). https://doi.org/10.5194/egusphere-2024-3746

Kim, Y.-H., Min, S.-K., Son, S.-W., & Choi, J. (2017). Attribution of the local Hadley cell widening in the Southern Hemisphere. *Geophysical Research Letters*, **44**(2), 1015–1024. https://doi.org/10.1002/2016GL072353

Komhyr, W. (1969). Electrical concentration cells for gas analysis. *Annales Geophysicae*, 25, 203–210.

Komhyr, W., & Harris, T. (1971). *Development of an ecc-ozonesonde, NOAA technical report*. ERL.

Laine, M. (2020). Introduction to dynamic linear models for time series analysis. In J.-P. Montillet & M. S. Bos (Eds.), *Geodetic time series analysis in earth sciences* (pp. 139–156). Springer International Publishing. https://doi.org/10.1007/978-3-030-21718-1_4

Laine, M., Latva-Pukkila, N., & Kyrölä, E. (2014). Analysing time-varying trends in stratospheric ozone time series using the state space approach. *Atmospheric Chemistry and Physics*, **14**(18), 9707–9725. https://doi.org/10.5194/acp-14-9707-2014

Leclair De Bellevue, J., Réchou, A., Baray, J. L., Ancellet, G., & Diab, R. D. (2006). Signatures of stratosphere to troposphere transport near deep convective events in the southern subtropics. *Journal of Geophysical Research*, **111**(D24), D24107. https://doi.org/10.1029/2005JD006947

Logan, J. A. (1994). Trends in the vertical distribution of ozone: An analysis of ozonesonde data. *Journal of Geophysical Research*, **99**(D12), 25553–25585. https://doi.org/10.1029/94JD02333

Lombardozzi, D., Levis, S., Bonan, G., Hess, P. G., & Sparks, J. P. (2015). The influence of chronic ozone exposure on global carbon and water cycles. *Journal of Climate*, **28**(1), 292–305. https://doi.org/10.1175/JCLI-D-14-00223.1

Loua, R. T., Bencherif, H., Bègue, N., Mbatha, N., Portafaix, T., Hauchecorne, A., et al. (2020). Surface temperature trend estimation over 12 sites in Guinea using 57 years of ground-based data. *Climate*, **8**(6), 68. https://doi.org/10.3390/cli8060068

Lu, J., Vecchi, G. A., & Reichler, T. (2007). Expansion of the Hadley cell under global warming. *Geophysical Research Letters*, **34**(6), L06805. https://doi.org/10.1029/2006GL028443

Manney, G. L., Millán, L. F., Santee, M. L., Wargan, K., Lambert, A., Neu, J. L., et al. (2022). Signatures of anomalous transport in the 2019/2020 arctic stratospheric polar vortex. *Journal of Geophysical Research: Atmospheres*, **127**(20), e2022JD037407. https://doi.org/10.1029/2022JD037407

Manney, G. L., Santee, M. L., Lambert, A., Millán, L. F., Minschwaner, K., Werner, F., et al. (2023). Siege in the Southern stratosphere: Hunga Tonga-Hunga Ha'apai water vapor excluded from the 2022 Antarctic polar vortex. *Geophysical Research Letters*, **50**(14), e2023GL0103855. https://doi.org/10.1029/2023GL0103855

Matsumura, Y., & Ananthaswamy, H. N. (2004). Toxic effects of ultraviolet radiation on the skin. *Toxicology and Applied Pharmacology*, **195**(3), 298–308. https://doi.org/10.1016/j.taap.2003.08.019

McCormick, M., Thomason, L., & Trepte, C. (1995). Atmospheric effects of the Mt Pinatubo eruption. *Nature*, **373**(6513), 399–404. https://doi.org/10.1038/373399a0

McPeters, R. D., & Labow, G. J. (2012). Climatology 2011: An MLS and Sonde derived ozone climatology for satellite retrieval algorithms. *Journal of Geophysical Research*, **117**(D10), D10303. https://doi.org/10.1029/2011JD017006

Millet, T., Bencherif, H., Portafaix, T., Bègue, N., Baron, A., Duflot, V., et al. (2025). Evidence of a transient ozone depletion event in the early Hunga plume above the Indian Ocean. *Atmospheric Chemistry and Physics*, **25**(18), 10887–10905. https://doi.org/10.5194/acp-25-10887-2025

Mills, G., Sharps, K., Simpson, D., Pleijel, H., Frei, M., Burkey, K., et al. (2018). Closing the global ozone yield gap: Quantification and cobenefits for multistress tolerance. *Global Change Biology*, **24**(10), 4869–4893. https://doi.org/10.1111/gcb.14381

Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent, R., et al. (2015). Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer. *Atmospheric Chemistry and Physics*, **15**(15), 8889–8973. https://doi.org/10.5194/acp-15-8889-2015

Nakamura, N. (1996). Two-dimensional mixing, edge formation, and permeability diagnosed in an area coordinate. *Journal of the Atmospheric Sciences*, **53**(11), 1524–1537. https://doi.org/10.1175/1520-0469(1996)053(1524:TDMEFA)2.0.CO;2

Nash, E. R., Newman, P. A., Rosenfield, J. E., & Schoeberl, M. R. (1996). An objective determination of the polar vortex using Ertel's potential vorticity. *Journal of Geophysical Research*, **101**(D5), 9471–9478. https://doi.org/10.1029/96JD00066

Network for the Detection of Atmospheric Composition Change (NDACC), & LATMOS/UVSQ. (2025). Sao UV-visible zenith-sky spectrometer data at Réunion island (St Denis) [Dataset]. Retrieved from https://ndacc.larc.nasa.gov/stations/reunion-island-st-denis-france

Oltmans, S. J., Johnson, B. J., Harris, J. M., Thompson, A. M., Liu, H. Y., Chan, C. Y., et al. (2004). Tropospheric ozone over the North Pacific from ozonesonde observations. *Journal of Geophysical Research*, **109**(D15), D07306. https://doi.org/10.1029/2003JD003466

Orphal, J., Staehelin, J., Tamminen, J., Braathen, G., De Backer, M.-R., Bais, A., et al. (2016). Absorption cross-sections of ozone in the ultraviolet and visible spectral regions: Status report 2015. *Journal of Molecular Spectroscopy*, **327**, 105–121. https://doi.org/10.1016/j.jms.2016.07.007

Pastel, M., Pommereau, J. P., Goutail, F., Richter, A., Pazmiño, A., Ionov, D., & Portafaix, T. (2014). Construction of merged satellite total O₃ and NO₂ time series in the tropics for trend studies and evaluation by comparison to NDACC SAOZ measurements. *Atmospheric Measurement Techniques*, **7**(10), 3337–3354. https://doi.org/10.5194/amt-7-3337-2014

Pickering, K. E., Thompson, A. M., Wang, Y., Tao, W.-K., McNamara, D. P., Kirchhoff, V. W. J. H., et al. (1996). Convective transport of biomass burning emissions over Brazil during TRACE A. *Journal of Geophysical Research*, **101**(D19), 23993–24012. https://doi.org/10.1029/96JD00346

Pitts, D. G., Cullen, A. P., & Hacker, P. D. (1977). Ocular effects of ultraviolet radiation from 295 to 365 nm. *Investigative Ophthalmology & Visual Science*, **16**(10), 932–939.

Pommereau, J. P., & Goutail, F. (1988). O₃ and NO₂ ground-based measurements by visible spectrometry during Arctic winter and spring 1988. *Geophysical Research Letters*, **15**(8), 891–894. https://doi.org/10.1029/GL015i008p00891

Portafaix, T., Morel, B., Bencherif, H., Baldy, S., Godin-Beekmann, S., & Hauchecorne, A. (2003). Fine-scale study of a thick stratospheric ozone lamina at the edge of the southern subtropical barrier. *Journal of Geophysical Research (Atmospheres)*, **108**(D6), 4196. https://doi.org/10.1029/2002JD002741

Posny, F., Metzger, J. M., & Baray, J. L. (2010). A Successful Change at La Reunion Island Station (21°S, 55.5°E). *SHADOZ Newsletter*, *11*.
Ranaivombola, M., Bègue, N., Bencherif, H., Millet, T., Sivakumar, V., Duflot, V., et al. (2023). Aerosol optical properties and types over Southern Africa and Reunion Island determined from ground-based and satellite observations over a 13-year period (2008–2021). *Remote Sensing*, 15(6), 1581. https://doi.org/10.3390/rs15061581

Randel, W. J., & Thompson, A. M. (2011). Interannual variability and trends in tropical ozone derived from SAGE II satellite data and SHADOZ ozonesondes. *Journal of Geophysical Research*, 116(D7), D07303. https://doi.org/10.1029/2010JD015195

Randriambelo, T., Baray, J.-L., & Baldy, S. (2000). Effect of biomass burning, convective venting, and transport on tropospheric ozone over the Indian Ocean: Reunion Island field observations. *Journal of Geophysical Research*, 105(D9), 11813–11832. https://doi.org/10.1029/1999JD0901097

Rowland, F. S. (1996). Stratospheric ozone depletion by chlorofluorocarbons (Nobel lecture). *Angewandte Chemie International Edition in English*, 35(16), 1786–1798. https://doi.org/10.1002/anie.199617861

Sauvage, B., Martin, R. V., van Donkelaar, A., Liu, X., Chance, K., Jaeglé, L., et al. (2007). Remote sensed and in situ constraints on processes affecting tropical tropospheric ozone. *Atmospheric Chemistry and Physics*, 7(3), 815–838. https://doi.org/10.5194/acp-7-815-2007

Sauvage, B., Martin, R. V., van Donkelaar, A., & Ziemke, J. R. (2007). Quantification of the factors controlling tropical tropospheric ozone and the South Atlantic maximum. *Journal of Geophysical Research*, 112(D11), 521. https://doi.org/10.1029/2006JD008008

Sitch, S., Cox, P. M., Collins, W. J., & Huntingford, C. (2007). Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. *Nature*, 448(7155), 791–794. https://doi.org/10.1038/nature06059

Sivakumar, V., Bencherif, H., Bègue, N., & Thompson, A. M. (2011). Tropopause characteristics and variability from 11 yr of shadoz observations in the southern tropics and subtropics. *Journal of Applied Meteorology and Climatology*, 50(7), 1403–1416. https://doi.org/10.1175/2011JAMC2453.1

Sivakumar, V., Portafaix, T., Bencherif, H., Godin-Beekmann, S., & Baldy, S. (2007). Stratospheric ozone climatology and variability over a southern subtropical site: Reunion Island (21°S; 55°E). *Annales Geophysicae*, 25(11), 2321–2334. https://doi.org/10.5194/angeo-25-2321-2007

Smit, H. G. J., Oltmans, S., Deshler, T., Tarasick, D., Johnson, B., Schmidlin, F., et al. (2012). SI2N/O3S-DQA Activity: Guidelines for homogenization of ozone sonde data. *SPARC-IGACO-IOC Report*.

Smit, H. G. J., Straeter, W., Johnson, B. J., Oltmans, S. J., Davies, J., Tarasick, D. W., et al. (2007). Assessment of the performance of ECC-ozonesondes under quasi-flight conditions in the environmental simulation chamber: Insights from the Juelich Ozone Sonde Intercomparison Experiment (JOSIE). *Journal of Geophysical Research*, 112(D19), D19306. https://doi.org/10.1029/2006JD007308

Smit, H. G. J., Thompson, A. M., & the ASOPOS 2.0 Panel. (2021). *Ozonesonde measurement principles and best operational practices (Technical Report No. 268)*. World Meteorological Organization (WMO). Retrieved from https://library.wmo.int/doc_num.php?explnum_id=10884

Soukharev, B. E., & Hood, L. L. (2006). Solar cycle variation of stratospheric ozone: Multiple regression analysis of long-term satellite data sets and comparisons with models. *Journal of Geophysical Research*, 111(D20), D20314. https://doi.org/10.1029/2006JD007107

Southern Hemisphere ADditional OZonesondes (SHADOZ). (2023). Southern hemisphere additional ozonesondes (shadoz) network data, version v06 [Dataset]. NASA/Goddard Space Flight Center. https://doi.org/10.57721/SHADOZ-V06

Stauffer, R. M., Thompson, A. M., Kollonige, D. E., Komala, N., Al-Ghazali, H. K., Risdianto, D. Y., et al. (2024). Dynamical drivers of free-tropospheric ozone increases over equatorial Southeast Asia. *Atmospheric Chemistry and Physics*, 24(9), 5221–5234. https://doi.org/10.5194/acp-24-5221-2024

Stauffer, R. M., Thompson, A. M., Kollonige, D. E., Tarasick, D. W., Van Malderen, R., Smit, H. G. J., et al. (2022). An examination of the recent stability of ozonesonde global network data. *Earth and Space Science*, 9(10), e2022EA002459. https://doi.org/10.1029/2022EA002459

Sterling, C. W., Johnson, B. J., Oltmans, S. J., Smit, H. G. J., Jordan, A. F., Cullis, P. D., et al. (2018). Homogenizing and estimating the uncertainty in NOAA's long-term vertical ozone profile records measured with the electrochemical concentration cell ozonesonde. *Atmospheric Measurement Techniques*, 11(6), 3661–3687. https://doi.org/10.5194/amt-11-3661-2018

Strode, S. A., Ziemke, J. R., Oman, L. D., Lamsal, L. N., Olsen, M. A., & Liu, J. (2019). Global changes in the diurnal cycle of surface ozone. *Atmospheric Environment*, 199, 323–333. https://doi.org/10.1016/j.atmosenv.2018.11.028

Tarasick, D. W., Smit, H. G. J., Thompson, A. M., Morris, G. A., Witte, J. C., Davies, J., et al. (2021). Improving ECC ozonesonde data quality: Assessment of Current methods and outstanding issues. *Earth and Space Science*, 8(3), e2019EA000914. https://doi.org/10.1029/2019EA000914

Thompson, A. M., Balashov, N. V., Witte, J. C., Coetzee, J. G. R., Thouret, V., & Posny, F. (2014). Tropospheric ozone increases over the southern Africa region: Bellwether for rapid growth in Southern Hemisphere pollution? *Atmospheric Chemistry and Physics*, 14(18), 9855–9869. https://doi.org/10.5194/acp-14-9855-2014

Thompson, A. M., MacFarlane, A. M., Morris, G. A., Yorks, J. E., Miller, S. K., Taubman, B. F., et al. (2010). Convective and wave signatures in ozone profiles over the equatorial Americas: Views from TC4 2007 and SHADOZ. *Journal of Geophysical Research*, 115(D10), D00J21. https://doi.org/10.1029/2009JD012909

Thompson, A. M., Miller, S. K., Tilmes, S., Kollonige, D. W., Witte, J. C., Oltmans, S. J., et al. (2012). Southern Hemisphere Additional Ozonesondes (SHADOZ) ozone climatology (2005–2009): Tropospheric and tropical tropopause layer (TTL) profiles with comparisons to OMI-based ozone products. *Journal of Geophysical Research*, 117(D23), D23301. https://doi.org/10.1029/2011JD016911

Thompson, A. M., Oltmans, S. J., Tarasick, D. W., von der Gathen, P., Smit, H. G., & Witte, J. C. (2011). Strategic ozone sounding networks: Review of design and accomplishments. *Atmospheric Environment*, 45(13), 2145–2163. https://doi.org/10.1016/j.atmosenv.2010.05.002

Thompson, A. M., Pickering, K. E., McNamara, D. P., Schoeberl, M. R., Hudson, R. D., Kim, J. H., et al. (1996). Where did tropospheric ozone over southern Africa and the tropical Atlantic come from in October 1992? Insights from TOMS, GTE TRACE A, and SAFARI 1992. *Journal of Geophysical Research*, 101(D19), 24251–24278. https://doi.org/10.1029/96JD01463

Thompson, A. M., Smit, H. G. J., Witte, J. C., Stauffer, R. M., Johnson, B. J., Morris, G., et al. (2019). Ozonesonde quality assurance: The josie-shadoz (2017) experience. *Bulletin of the American Meteorological Society*, 100(1), 155–171. https://doi.org/10.1175/BAMS-D-17-0311.1

Thompson, A. M., Stauffer, R. M., Kollonige, D. E., Ziemke, J. R., Cazorla, M., Wolff, P., & Sauvage, B. (2025). Tropical Ozone Trends (1998 to 2023): A Synthesis from SHADOZ, IAGOS and OMI/MLS Observations. *EGUsphere*, 2025, 1–34. https://doi.org/10.5194/egusphere-2024-3761

Thompson, A. M., Stauffer, R. M., Wargan, K., Witte, J. C., Kollonige, D. E., & Ziemke, J. R. (2021). Regional and seasonal trends in tropical ozone from SHADOZ profiles: Reference for models and satellite products. *Journal of Geophysical Research: Atmospheres*, 126(22), e2021JD034691. https://doi.org/10.1029/2021JD034691

Thompson, A. M., Tao, W.-K., Pickering, K. E., Scala, J. R., & Simpson, J. (1997). Tropical deep convection and ozone formation. *Bulletin of the American Meteorological Society*, 78(6), 1043–1054. https://doi.org/10.1175/1520-0477(1997)078(1043:TDCAOF)2.0.CO;2
Thompson, A. M., Witte, J. C., Hudson, R. D., Guo, H., Herman, J. R., & Fujiwara, M. (2001). Tropical tropospheric ozone and biomass burning. *Science*, **291**(5511), 2128–2132. https://doi.org/10.1126/science.291.5511.2128

Thompson, A. M., Witte, J. C., McPeters, R. D., Oltmans, S. J., Schmidlin, F. J., Logan, J. A., et al. (2003). Southern Hemisphere Additional Ozonesondes (SHADOZ) 1998–2000 tropical ozone climatology 1. Comparison with Total Ozone Mapping Spectrometer (TOMS) and ground-based measurements. *Journal of Geophysical Research*, **108**(D2), 8238. https://doi.org/10.1029/2001JD000967

Thompson, A. M., Witte, J. C., Oltmans, S. J., Schmidlin, F. J., Logan, J. A., Fujiwara, M., et al. (2003). Southern Hemisphere Additional Ozonesondes (SHADOZ) 1998–2000 tropical ozone climatology 2. Tropospheric variability and the zonal wave-one. *Journal of Geophysical Research*, **108**(D2), 8241. https://doi.org/10.1029/2002JD002241

Thompson, A. M., Witte, J. C., Sterling, C., Jordan, A., Johnson, B. J., Oltmans, S. J., et al. (2017). First reprocessing of Southern hemisphere additional ozonesondes (SHADOZ) ozone profiles (1998–2016): 2. Comparisons with satellites and ground-based instruments. *Journal of Geophysical Research: Atmospheres*, **122**(23), 13000–13025. https://doi.org/10.1002/2017JD027406

Toihir, A. M., Bencherif, H., Sivakumar, V., El Amraoui, L., Portafaix, T., & Mbatha, N. (2015). Comparison of total column ozone obtained by the IASI-MetOp satellite with ground-based and OMI satellite observations in the southern tropics and subtropics. *Annales Geophysicae*, **33**(9), 1135–1146. https://doi.org/10.5194/angeo-33-1135-2015

Toihir, A. M., Portafaix, T., Sivakumar, V., Bencherif, H., Pazmiño, A., & Bègue, N. (2018). Variability and trend in ozone over the southern tropics and subtropics. *Annales Geophysicae*, **36**(2), 381–404. https://doi.org/10.5194/angeo-36-381-2018

Turner, M. C., Jerrett, M., Pope, C. A., Krewski, D., Gapstur, S. M., Diver, W. R., et al. (2016). Long-Term ozone exposure and mortality in a large prospective Study. *American Journal of Respiratory and Critical Care Medicine*, **193**(10), 1134–1142. https://doi.org/10.1164/rccm.201508-1633OC

Van Malderen, R., Thompson, A. M., Kollonige, D. E., Stauffer, R. M., Smit, H. G. J., Maillard Barras, E., et al. (2025). Global ground-based tropospheric ozone measurements: Reference data and individual site trends (2000–2022) from the TOAR-II/HEGIFTOM Project (pp. 1–53). EGUsphere. https://doi.org/10.5194/egusphere-2024-3736

Verma, S., Yadava, P. K., Lal, D. M., Mall, R. K., Kumar, H., & Payra, S. (2021). Role of lightning NOₓ in ozone Formation: A review. *Pure and Applied Geophysics*, **178**(4), 1425–1443. https://doi.org/10.1007/s00024-021-02710-5

Vigouroux, C., Stavrakou, T., Whaley, C., Dils, B., Duflot, V., Hermans, C., et al. (2012). FTIR time-series of biomass burning products (HCN, C₂H₅, C₂H₂, CH₃OH, and HCOOH) at Reunion Island (21°S, 55°E) and comparisons with model data. *Atmospheric Chemistry and Physics*, **12**(21), 10367–10385. https://doi.org/10.5194/acp-12-10367-2012

Weber, M., Arosio, C., Coldewey-Egbers, M., Fioletov, V. E., Frith, S. M., Wild, J. D., et al. (2022). Global total ozone recovery trends attributed to ozone-depleting substance (ODS) changes derived from five merged ozone datasets. *Atmospheric Chemistry and Physics*, **22**(10), 6843–6859. https://doi.org/10.5194/acp-22-6843-2022

Weber, M., Coldewey-Egbers, M., Fioletov, V. E., Frith, S. M., Wild, J. D., Burrows, J. P., et al. (2018). Total ozone trends from 1979 to 2016 derived from five merged observational datasets—The emergence into ozone recovery. *Atmospheric Chemistry and Physics*, **18**(3), 2097–2117. https://doi.org/10.5194/acp-18-2097-2018

West, J. J., Fiore, A. M., Naik, V., Horowitz, L. W., Schwarzkopf, M. D., & Mauzerall, D. L. (2007). Ozone air quality and radiative forcing consequences of changes in ozone precursor emissions. *Geophysical Research Letters*, **34**(6), 1–5. https://doi.org/10.1029/2006GL029173

Witte, J. C., Thompson, A. M., Smit, H. G. J., Fujiwara, M., Posny, F., Coetzee, G. J. R., et al. (2017). First reprocessing of Southern Hemisphere ADditional OZonesondes (SHADOZ) profile records (1998–2015): 1. Methodology and evaluation. *Journal of Geophysical Research: Atmospheres*, **122**(12), 6611–6636. https://doi.org/10.1002/2016JD026403

Witte, J. C., Thompson, A. M., Smit, H. G. J., Vömel, H., Posny, F., & Stübi, R. (2018). First reprocessing of Southern hemisphere ADditional OZonesondes profile records: 3. Uncertainty in ozone profile and total column. *Journal of Geophysical Research: Atmospheres*, **123**(6), 3243–3268. https://doi.org/10.1002/2017JD027791

WMO. (2018). Scientific assessment of ozone depletion: 2018 (Global Ozone Research and Monitoring Project-Report No. 58). World Meteorological Organization. Retrieved from https://library.wmo.int/doc_num.php?explnum_id=5704

WMO. (2022). Scientific assessment of ozone depletion: 2018 (GAW Report no. 278). World Meteorological Organization. Retrieved from https://library.wmo.int/idurl/4/58360

Young, P. J., Naik, V., Fiore, A. M., Gaudel, A., Guo, J., Lin, M. Y., et al. (2018). Tropospheric ozone assessment report: Assessment of global-scale model performance for global and regional ozone distributions, variability, and trends. *Elementa: Science of the Anthropocene*, **6**, 10. https://doi.org/10.1525/elementa.265

Zeng, G., Querel, R., Shiona, H., Poyraz, D., Van Malderen, R., Geddes, A., et al. (2024). Analysis of a newly homogenised ozonesonde dataset from Lauder, New Zealand. *Atmospheric Chemistry and Physics*, **24**(10), 6413–6432. https://doi.org/10.5194/acp-24-6413-2024

Zerefos, C. S., Tourpali, K., Bojkov, B. R., Balis, D. S., Rognerund, B., & Isaksen, I. S. A. (1997). Solar activity-total column ozone relationships: Observations and model studies with heterogeneous chemistry. *Journal of Geophysical Research*, **102**(D1), 1561–1569. https://doi.org/10.1029/96JD02395

Zhang, Y., Cooper, O. R., Gaudel, A., Thompson, A. M., Nédélec, P., Ogino, S.-Y., & West, J. J. (2016). Tropospheric ozone change from 1980 to 2010 dominated by equatorward redistribution of emissions. *Nature Geoscience*, **9**(12), 875–879. https://doi.org/10.1038/ngeo2827

Ziemke, J. R., & Chandra, S. (2003). La Nina and El Nino—Induced variabilities of ozone in the tropical lower atmosphere during 1970–2001. *Geophysical Research Letters*, **30**(3), 1142. https://doi.org/10.1029/2002GL016387

Ziemke, J. R., Chandra, S., Duncan, B. N., Froidevaux, L., Bhartia, P. K., Levelt, P. F., & Waters, J. W. (2006). Tropospheric ozone determined from Aura OMI and MLS: Evaluation of measurements and comparison with the Global Modeling Initiative's Chemical Transport Model. *Journal of Geophysical Research*, **111**(D19), D19303. https://doi.org/10.1029/2006JD007089

Ziemke, J. R., Oman, L. D., Strode, S. A., Douglass, A. R., Olsen, M. A., McPeters, R. D., et al. (2019). Trends in global tropospheric ozone inferred from a composite record of TOMS/OMI/MLS/OMPS satellite measurements and the MERRA-2 GMI simulation. *Atmospheric Chemistry and Physics*, **19**(5), 3257–3269. https://doi.org/10.5194/acp-19-3257-2019

Zou, L., Hoffmann, L., Müller, R., & Spang, R. (2023). Variability and trends of the tropical tropopause derived from a 1980—2021 multi-reanalysis assessment. *Frontiers in Earth Science*, **11**, 1177502. https://doi.org/10.3389/feart.2023.1177502