# Arctic tropospheric ozone: assessment of current knowledge and model performance

Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, et al.

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## To cite this version:

Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, et al. "Arctic tropospheric ozone: assessment of current knowledge and model performance. Atmospheric Chemistry and Physics, 2023, 23 (1), pp.637-661. 10.5194/acp-23-637-2023. insu-03682231v2

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Arctic tropospheric ozone: assessment of current
knowledge and model performance

Cynthia H. Whaley¹, Kathy S. Law², Jens Liengaard Hjorth³, Henrik Skov³, Stephen R. Arnold⁴,
Joakim Langner⁵, Jakob Boyd Pernov³,ª, Garance Bergeron⁴, Ilann Bourgeois²,⁰,²¹,ª,ᵇ,
Jesper H. Christensen³, Rong-You Chien⁶, Makoto Deushi¹¹, Xinyi Dong⁶, Peter Effertz¹⁶,
Gregory Faluvegi⁷,⁸, Mark Flanner⁹, Joshua S. Fu⁶, Michael Gauss¹⁰, Greg Huey²², Ulas Im³,
Rigel Kivi¹⁹, Louis Marelle², Tatsuo Onishi², Naga Oshima¹¹, Irina Petropavlovskikh¹⁶,¹⁷,
Jeff Peischl²⁰,²¹, David A. Plummer¹, Luca Pozzoli¹²,ᶜ, Jean-Christophe Raut², Tom Ryerson²³,
Ragnhild Skeie¹³, Sverre Solberg¹⁸, Manu A. Thomas⁵, Chelsea Thompson²¹, Kostas Tsigaridis⁸,
Svetlana Tsyro¹⁰, Steven T. Turnock¹⁴,⁴, Knut von Salzen¹, and David W. Tarasick¹⁵

¹Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada

²LATMOS/IPSL, Sorbonne Université, UVSQ, CNRS, Paris, France

³Department of Environmental Science/Interdisciplinary Centre for Climate Change, Aarhus University,
Frederiksborgvej 400, Roskilde, Denmark

⁴Institute for Climate and Atmospheric Science, School of Earth and Environment,
University of Leeds, Leeds, UK

⁵Swedish Meteorological and Hydrological Institute, Norrköping, Sweden

⁶Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee, USA

⁷NASA Goddard Institute for Space Studies, New York, NY, USA

⁸Center for Climate Systems Research, Columbia University; New York, USA

⁹Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA

¹⁰Division for Climate Modelling and Air Pollution, Norwegian Meteorological Institute, Oslo, Norway

¹¹Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan

¹²European Commission, Joint Research Centre, Ispra, Italy

¹³CICERO Center for International Climate and Environmental Research, Oslo, Norway

¹⁴Met Office Hadley Centre, Exeter, UK

¹⁵Air Quality Research Division, Environment and Climate Change Canada, Toronto, ON, Canada

¹⁶Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado, Boulder, CO, USA

¹⁷National Oceanic and Atmospheric Administration (NOAA) ESRL Global Monitoring Laboratory,
Boulder, CO, USA

¹⁸Norwegian Institute for Air Research (NILU), Kjeller, Norway

¹⁹Finnish Meteorological Institute, Sodankylä, Finland

²⁰Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder,
Boulder, CO, USA

²¹NOAA Chemical Sciences Laboratory, Boulder, CO, USA

²²School of Earth and Atmospheric Sciences, Georgia Tech, Atlanta, Georgia, USA

²³Scientific Aviation, Boulder, CO, USA

ªnow at: Extreme Environments Research Laboratory, École Polytechnique fédérale de Lausanne,
1951 Sion, Switzerland

ᵇnow at: Plant Ecology Research Laboratory, École Polytechnique fédérale de Lausanne,
1015 Lausanne, Switzerland

ᶜnow at: FINCONS SPA, Via Torri Bianche 10, 20871 Vimercate, Italy

**Correspondence:** Cynthia H. Whaley (cynthia.whaley@ec.gc.ca)
Received: 30 April 2022 – Discussion started: 17 May 2022
Revised: 10 November 2022 – Accepted: 14 December 2022 – Published: 16 January 2023

**Abstract.** As the third most important greenhouse gas (GHG) after carbon dioxide (CO₂) and methane (CH₄), tropospheric ozone (O₃) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O₃ in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O₃ seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O₃ depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O₃ depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O₃ and its precursor species. The MMM underestimates Arctic surface O₃ by 5% to 15% depending on the location. The vertical distribution of tropospheric O₃ is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O₃ within a range of ±50% depending on the model and the altitude. The MMM performs best, within ±8% for most locations and seasons. However, nearly all models overestimate O₃ near the tropopause (~300 hPa or ~8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O₃ at high latitudes. For example, the MMM is biased high by about 20% at Eureka. Observed and simulated O₃ precursors (CO, NOₓ, and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOₓ but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O₃ matches the observed O₃ reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O₃.

# 1 Introduction

Tropospheric ozone (O₃) is the third most important greenhouse gas (GHG) after CO₂ and methane (IPCC, 2021), and it is an air pollutant causing damage to human health (World Health Organization (WHO), 2021). It also causes damage to vegetation following dry deposition to the surface (U.S. EPA, 2013). However, our knowledge about the sources and sinks of tropospheric O₃ is still uncertain (AMAP, 2015, 2022; Gaudel et al., 2018), in particular in regions where fewer observations exist and where our understanding of key processes is still evolving. The Arctic is one such region where few long-term measurements of O₃ exist and measurements of compounds that are important for producing and destroying O₃ in the atmosphere are scarce at the surface and even more so in the free troposphere. Progress has been made recently in terms of our understanding of certain processes, and a picture is emerging about the distribution of Arctic tropospheric O₃ as well as seasonal cycles and trends at different locations (e.g., Young et al., 2018; Tarasick et al., 2019b). In particular, the connection between surface O₃ depletion episodes and halogens is now well-established (e.g., Simpson et al., 2007; Abbatt et al., 2012).

However, the role of natural cycles in the Arctic O₃ budget relative to O₃ produced from anthropogenic emissions and how that relationship is changing in response to rapid warming in the Arctic are still uncertain. Arctic warming and associated development in the Arctic are also driving changes in local anthropogenic emissions, which could already be leading to changes in the relative contributions of O₃ produced due to long-range transport of midlatitude anthropogenic emissions and O₃ produced from within or near Arctic anthropogenic emissions. Increases in emissions, such as from shipping (Gong et al., 2018) or boreal fires, can affect Arctic air quality (Schmale et al., 2018).

Ozone radiative forcing resulting from changes in tropospheric O₃ in the Arctic is highly sensitive to altitude. The sensitivity of the Arctic O₃ vertical profile and resultant forcing from particular anthropogenic emission sources and regions vary substantially with altitude (Rap et al., 2015). Arctic surface O₃ may be most sensitive to European or local sources (Sand et al., 2015; AMAP, 2015, 2022), whereas emissions from North American and Asian sources are more important in the middle and upper troposphere (Monks et al., 2015; Wespes et al., 2012). Therefore, a combination of varied source sensitivities in the vertical profile and the increased efficacy of longwave O₃ forcing with altitude in the
troposphere leads to a complex picture in terms of drivers of climate forcing by Arctic O$_{3}$. The presence of temperature inversions in the Arctic lower troposphere may result in negative local forcing (Rap et al., 2015; Flanner et al., 2018), in particular for local sources such as shipping (Marelle et al., 2018). Hence, to improve the quantification of O$_{3}$ radiative effects in the Arctic there is a need first to assess model performance in terms of seasonal cycles and vertical distributions. The annual mean vertical distributions of O$_{3}$ and CO were examined in AMAP (2022) and Whaley et al. (2022) compared to the Tropospheric Emission Spectrometer (TES) and Measurement of Pollution in the Troposphere (MOPITT) satellite retrievals. Those studies showed good agreement between models and satellite measurements for O$_{3}$ in the free troposphere, where it is a strong GHG.

This paper assesses the current state of knowledge about the dynamics of Arctic tropospheric O$_{3}$ and the ability of a suite of current chemistry-transport and chemistry-climate models to simulate seasonal cycles of O$_{3}$ and selected precursors. We first review our current understanding of sources and sinks of Arctic tropospheric O$_{3}$ in Sect. 2. We summarize the models used in this study in Sect. 3 and the recent findings from satellite observations in Sect. 4. We then examine the extent to which our understanding of Arctic tropospheric O$_{3}$ can explain observed seasonal cycles at different surface sites in the Arctic and assess the ability of models to simulate observed distributions (Sect. 5). We also examine vertical distributions of O$_{3}$ and its precursors and the extent to which models are able to capture observed seasonal variations (Sect. 6). Finally, conclusions are presented in Sect. 7. Trends in Arctic tropospheric O$_{3}$ over the last 20-30 years and possible changes in seasonal cycles will be presented in a follow-on study and compared to results from a subset of these models.

## 2 Arctic O$_{3}$: sources and sinks

This section reviews tropospheric O$_{3}$ sources and sinks that are particularly relevant to the Arctic region, and many of these processes are shown in the schematic in Fig. 1.

### 2.1 Ozone sources

Tropospheric O$_{3}$ is a secondary air pollutant, which is not directly emitted but produced from the photochemical reactions of anthropogenic and natural precursor emissions of volatile organic compounds (VOCs), CO, and CH$_{4}$ in the presence of NO$_{x}$. Besides significant anthropogenic sources of these O$_{3}$ precursors, there are also important natural sources for these species, such as boreal fires, lightning, vegetation, and transport of O$_{3}$ from the stratosphere (Fig. 1), which show marked seasonal and interannual variations. Away from the surface and in remote environments the tropospheric O$_{3}$ lifetime is around 20 d or more (Young et al., 2013), which facilitates the long-range transport of O$_{3}$ in the

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Schematic of Arctic tropospheric O$_{3}$ sources, sinks, and relevant processes.</figcaption></figure>

troposphere. Production of O$_{3}$ from lower-latitude emission sources and its subsequent transport to the Arctic constitute a substantial source of Arctic tropospheric O$_{3}$ (Hirdman et al., 2010), where the dry Arctic conditions and stably stratified atmosphere further prolong the O$_{3}$ lifetime. In addition, the stratosphere-troposphere exchange of O$_{3}$ makes a substantial contribution to the Arctic O$_{3}$ budget. The weak in situ O$_{3}$ formation in the Arctic relative to lower latitudes increases the relative importance of this exchange.

Downward transport of O$_{3}$ from the stratosphere is an important source of O$_{3}$ in the Arctic troposphere and may be key in driving seasonality in Arctic tropospheric O$_{3}$ (Shapiro et al., 1987; Hess and Zbinden, 2013; Ancellet et al., 2016). Based on modeling, Liang et al. (2009) show that in spring (March and April), most of the O$_{3}$ in the Arctic upper troposphere originates from stratospheric injection (78%) and that 20%-25% of surface O$_{3}$ originates from direct injection of O$_{3}$ or the injection of NO$_{x}$ and secondary O$_{3}$ formation. Analysis of observations by Tarasick et al. (2019a) is consistent with this picture. Global model simulations conducted as part of the Coupled Model Intercomparison Project Phase 6 suggest an increase in near-surface O$_{3}$ over the Arctic during the 21st century, driven by increased stratospheric O$_{3}$ import into the troposphere, particularly in winter (Zanis et al., 2022). In contrast, during summer, in situ production in the Arctic contributes a significant fraction, with a model study estimating a contribution of more than 50% of O$_{3}$ in the Arctic boundary layer and 30%-40% in the free troposphere for the month of July (Walker et al., 2012). Methane (CH$_{4}$) is a key precursor for tropospheric O$_{3}$ via its oxidation in the presence of sufficient NO$_{x}$. Increases in anthropogenic CH$_{4}$ emissions are estimated to be responsible for 44 ± 12% of the global tropospheric ozone radiative forcing from the pre-industrial era to present day (Stevenson et al., 2013). Fiore et al. (2008) estimated that anthropogenic CH$_{4}$ emissions contribute 15% to the annual average total global O$_{3}$ burden (including natural and anthropogenic sources). Based
on parameterized source-receptor sensitivities for a range of CMIP6 SSP scenarios, Turnock et al. (2019) illustrated the significant contribution of CH₄ to future O₃ concentration reductions at high latitudes under future conditions with lower NOₓ concentrations. Using a similar approach based on parameterized responses to O₃ precursor emission perturbations, it was found that CH₄ accounts for approximately 40% of the Arctic O₃ response to precursor emission perturbations (AMAP, 2015). Thawing permafrost and release from organic deposits in shallow Arctic Ocean waters in a warmer climate represent a new source of methane (Isaksen et al., 2014).

Import of O₃ and its precursors from lower latitudes associated with episodes of long-range transport of anthropogenic or biomass burning pollution leads to enhancements in Arctic tropospheric O₃ (Wespes et al., 2012; Monks et al., 2015; Ancellet et al., 2016). Whilst very low levels of NOₓ within the Arctic, away from local sources, often limit local O₃ production, the release of NOₓ from thermal decomposition of peroxy-acetyl nitrate (PAN) (an important NOₓ reservoir) imported from lower latitudes can lead to in situ production of O₃, particularly in the warmer Arctic summer lower troposphere (Wespes et al., 2012; Walker et al., 2012; Arnold et al., 2015). Investigation of long-range transport of O₃ precursors has shown efficient export of PAN from East Asia to the North Pacific, with relative contributions to long-range O₃ transport of 35% in spring and 25% in summer (Jiang et al., 2016). Ship observations over the Arctic Ocean and Bering Sea also identified events of long-range pollution transport with enhancements in O₃ (Kanaya et al., 2019).

Recently, there has been progress in improving knowledge of local O₃ precursor sources. Surface O₃ in summer is influenced by shipping NOₓ emissions along the northern Norwegian coast (Marelle et al., 2016; Marelle et al., 2018) and the Northwest Passage (Aliabadi et al., 2015). Additionally, Tuccella et al. (2017) showed that background O₃ is influenced by emissions downwind of oil and gas extraction platforms in the southern Norwegian Sea. Natural sources of Arctic tropospheric O₃ precursors include lightning NOₓ, emissions of NOₓ and reactive VOCs from the snowpack (Honrath et al., 1999; Guimbaud et al., 2002; Hornbrook et al., 2016; Pernov et al., 2021), natural emissions of VOCs from high-latitude vegetation (Holst et al., 2010; Ghirardo et al., 2020), and the sea surface microlayer (Mungall et al., 2017). Evidence from both observations and models suggests that boreal fires are also an important source of O₃ precursors and NOₓ reservoir species like PAN in spring and summer, with impacts on Arctic O₃ (Thomas et al., 2013; Arnold et al., 2015; Viatte et al., 2015; Ancellet et al., 2016).

## 2.2 Ozone sinks

Photochemical loss of O₃ is mainly via photolysis in the presence of water vapor or direct reaction of O₃ with hydroperoxyl (HO₂) or hydroxyl radicals (OH). Photochemical

destruction involving HO₂ may be particularly important in the Arctic where water vapor abundances are low (Arnold et al., 2015). Where local emission sources give rise to high NOₓ concentrations in urban regions or regions of shipping activity, O₃ loss via titration with NO can be dominant (Thorp et al., 2021; Raut et al., 2022). Dry deposition of O₃ and its precursors to ice and ocean surfaces is slower than to vegetated terrestrial surfaces (Fig. 1). Van Dam et al. (2016) reported O₃ dry deposition velocities that were 5 times higher over Arctic snow-free tundra in the summer months at Toolik Lake (northern Alaska) compared to the snow-covered ground. Dry deposition, combined with possible chemical loss (e.g., involving biogenic volatile organic compounds, BVOCs) producing lower O₃ concentrations during stable (lower light) night conditions may explain the different diurnal cycle observed at this tundra site compared to Arctic coastal locations. Interestingly, gradient studies at the NOAA Barrow Observatory near Utqiaġvik and at Zeppelin showed a positive gradient with height during O₃ depletion events (ODEs) and atmospheric mercury depletion events (AMDEs), suggesting that O₃ was removed at the surface due to fast photochemical reactions at or close to snow surfaces initiated by the release of halogen species (Skov et al., 2006; Solberg et al., 1996; Berg et al., 2003; Eneroth et al., 2007). During ODEs at Arctic sites in the Canadian archipelago (Alert, Resolute, and Eureka), vertical profiles show that ozone is typically uniformly depleted in the boundary layer, whereas a positive gradient is observed above the boundary layer (Tarasick and Bottenheim, 2002).

During Arctic spring, photochemical cycling of halogens in so-called “bromine explosion” events leads to rapid depletion of surface O₃ to low or near-zero concentrations (Barrie et al., 1988; Skov et al., 2004; Helmig et al., 2007; Simp-

<figure><img src="image_6.png" /></figure>

**Figure 2.** Ozone lidar measurements from Eureka in the spring of 2008 showing effects of large-scale meteorology including low O₃ in the lower troposphere when air masses originate from the north over the Arctic Ocean and enhanced O₃ during downward transport into the Arctic boundary layer when the airflow was from the south over mountains. From Fig. 3 in Seabrook and Whiteway (2016).
son et al., 2007). These phenomena are observed at Arctic coastal locations and in the Arctic Ocean (Bottenheim et al., 2009; Jacobi et al., 2010) in March–April and attributed to bromine (halogen) sources linked to Arctic sea ice, coupled with stable surface temperature inversions (e.g., Fig. 1; Hermann et al., 2019). Some model studies were able to explain major depletion events in simulations by introducing the wind-induced release of bromine from the snowpack and have shown that both blowing snow and the snowpack are important sources of bromine during the spring (e.g., Yang et al., 2010; Toyota et al., 2011; Yang et al., 2020; Huang et al., 2020; Swanson et al., 2022). Figure 2 shows the vertical extent of low O₃ episodes observed by lidar at Europea in northern Canada. On 7 May, low O₃ concentrations were observed, and back trajectories showed that air masses came in from the ice-covered Arctic Ocean and had been in contact with the surface multiple times during the previous 6 d, whereas the concentrations were high on 9 May, when air came down from the mountains located to the south (Seabrook and Whiteway, 2016). Peterson et al. (2018) showed that active halogen chemistry and related O₃ depletion can also occur up to 200 km inland over snow-covered tundra in Alaska. Simpson et al. (2018) reported high levels of bromine oxide (BrO) at Utqiaġvik occurring earlier in February in air masses originating from the Arctic Ocean polar night. Their findings suggest a dark wintertime source of reactive bromine (halogens) that could feed halogen photochemistry at lower latitudes as the sun returns. This dark mechanism was also observed over sea ice in Antarctica by Nerentorp Mastromonaco et al. (2016).

In addition, whilst earlier studies proposed indirect evidence that O₃ and gaseous elemental mercury (Hg⁰) are removed by reaction with Br atoms (e.g., Skov et al., 2004; Skov et al., 2020; Dastoor et al., 2008), Wang et al. (2019) showed, for the first time, a direct connection between O₃ and Hg⁰ with atomic bromine (Br) during O₃ and Hg⁰ depletion episodes at Utqiaġvik on the northern coast of Alaska (see Fig. 3) where O₃ and Hg⁰ are removed in competing reactions with Br. Here, the Br/BrO ratio anti-correlates with O₃ concentrations, and box modeling confirms that O₃ is removed by Br.

This result is significant since the main source of halogens in the Arctic is the release from refreezing sea ice, blowing snow over sea ice, heterogeneous reactions of aerosol particles, and snowpack recycling (Petersen et al., 2016; Peterson et al., 2017, Wang et al., 2017; Yang et al., 2020). Burd et al. (2017) found a strong relationship between the end of the reactive bromine season and snowmelt timing. In the future, continued decreases in Arctic sea ice extent or the relative distributions of multi-year and seasonal sea ice cover (Peterson et al., 2019), coupled with increases in the length of the snow-free season over land, could influence the magnitude and seasonality of O₃ sinks via changes in halogen fluxes or dry deposition fluxes to tundra and ocean rather than snow and ice surfaces.

<figure><img src="image_7.png" /><figcaption>**Figure 3.** Time series at Utqiaġvik on 20 March 2012 of measured (a) atomic bromine (Br) and bromine monoxide (BrO), (b) Br/BrO ratios, and O₃. Error bars represent propagated measurement uncertainties. From Fig. 2 in Wang et al. (2019)(EPS figure provided for the report).</figcaption></figure>

It has also recently been shown that substantial O₃ depletion can occur due to reactions with iodine (Benavent et al., 2022). That study, which was based on ship measurements during the MOSAiC expedition in March to October 2020, found that iodine contributed more to O₃ loss than bromine, thus highlighting how the dynamics of high Arctic O₃ depletion are still not fully elucidated.

## 3 AMAP models and simulations

To evaluate our process understanding of controls on the Arctic tropospheric O₃ budget and distribution, we evaluate a subset of the same model simulations that were used in AMAP (2022) and by Whaley et al. (2022). A total of 12 atmospheric models participated in this study: seven chemical transport models (DEHM, EMEP MSC-W, GEOS-Chem, MATCH, MATCH-SALSA, OsloCTM, WRF-Chem) and five chemistry–climate models (CESM, CMAM, GISS-E2.1, MRI-ESM2, and UKESM1), with simulations of the years 2014–2015 for comparisons to observations. All models used the same set of anthropogenic emissions called ECLIPSEv6b (AMAP 2022), though they had different sources for fire, biogenic emissions, and meteorology (see Table S1 in the Supplement). The years 2014–2015 were chosen for model validation as it was the most recent time period that the ECLIPSE v6b historical emissions were available when the model simulations were being set up. All participating models prescribe CH₄ concentrations based on box model results, which are, in turn, based on the ECLIPSEv6b anthropogenic
CH₄ emissions and various assumptions on natural CH₄ emissions (Olivié et al., 2021; Prather et al., 2012). Models then allow CH₄ to take part in photochemical processes. The participating models have varying degrees of spatial resolution and chemical complexity; air-quality-focused models, such as DEHM, EMEP MSC-W, GEOS-Chem, MATCH, and WRF-Chem, have detailed HOₓ–NOₓ–hydrocarbon O₃ chemistry, with speciated VOCs and secondary aerosol formation, and they tend to run at higher resolution. The Earth system models GISS-E2.1, MRI-ESM2, and UKESM1 also contain this level of tropospheric chemistry, though they run globally at coarser resolution, whereas climate-focused models like CMAM run at a coarse resolution and have simplified tropospheric chemistry in order to be able to run for long periods. For example, CMAM’s tropospheric chemistry consists only of CH₄–NOₓ–O₃ chemistry, with no VOCs.

As mentioned above, Arctic tropospheric O₃ is heavily influenced by imports from the stratosphere. The models vary, too, in their representation of the stratosphere. Only a subset of participating models has a fully simulated stratosphere. CMAM, MRI-ESM2, GISS-E2.1, OsloCTM, and UKESM1 contain relatively complete stratospheric O₃ chemistry (NOₓ, NOₓ, Clₓ, Brₓ chemistry that controls stratospheric O₃). Other models have a simplified stratosphere, such as GEOS-Chem which has a linearized stratospheric chemistry scheme (LINOZ; McLinden et al., 2000) and WRF-Chem which specifies stratospheric concentrations from climatologies. Finally, several models have no stratosphere or stratospheric chemistry at all (e.g., DEHM and EMEP MSC-W). Most atmospheric models, including all of the models in this study, do not yet contain Arctic tropospheric bromine chemistry and thus cannot simulate the surface-level bromine-driven O₃ depletion events that occur during spring. However, there are research versions of some models which are starting to contain this chemistry (e.g., Parrella et al., 2012; Falk and Sinnhuber, 2018; Badia et al., 2021).

These same 12 model simulations were also evaluated against a different set of measurements in AMAP (2022) and Whaley et al. (2022). Those studies focused on many SLCF species over the Northern Hemisphere and generally reported model biases for the annual mean concentrations. They found that all models overestimated surface O₃ concentrations at midlatitudes but that there were both overestimation and underestimation in the Arctic. Particularly, models overestimated surface O₃ in the western Arctic (e.g., Alaska), particularly in the summertime, but were better able to simulate the surface O₃ seasonal cycle in the eastern Arctic (e.g., northern Europe). They also found that model biases were small throughout the free troposphere when compared to remote measurements from the TES satellite instrument.

In the next sections, these models are compared with observations of O₃ (at measurement sites located in Fig. 4) and its precursors either individually or as the multi-model median (MMM) – whereby the median of all 12 atmospheric

models at the measurement locations is shown unless otherwise noted. The model output was selected from the model grid box that contains the latitude and longitude of the observation location without any spatial interpolation.

## 4 Arctic-wide tropospheric distributions from satellite data

Despite the potential limitations of some satellite data products at high latitudes, several studies have exploited satellite observations to investigate tropospheric O₃ and precursor distributions as well as trends relevant to the Arctic. Pommier et al. (2012) presented Infrared Atmospheric Sounding Interferometer (IASI) retrievals of 0–8 and 0–12 km sub-column O₃ for the Arctic in spring and summer 2008. These showed widespread enhancements in the springtime (March–April) tropospheric O₃ column compared with summer (June–July), particularly over northeastern Siberia, northern Canada, and the Arctic Ocean. Generally, good agreement with in situ aircraft profiles was demonstrated, but low thermal contrast between the Arctic surface and boundary layer was found to produce bias in IASI retrievals compared with aircraft measurements in the Arctic lower troposphere. Wespes et al. (2012) showed that IASI was able to detect enhancements in midlatitude-sourced O₃ enhancements during summer at the edge of the Arctic, but also showed a lack of sensitivity over snow and ice surfaces, potentially resulting in missing some O₃ enhancements. Sodemann et al. (2011) analyzed the cross-polar transport of a large pollution plume originating from Asia during the summer of 2008 using IASI CO retrievals. IASI was able to detect features and structures of the plume consistent with in situ aircraft data.

Satellite observations are also useful in evaluating the sources and export of O₃ precursors from midlatitude source regions and their subsequent transport to the Arctic. Tropospheric NO₂ columns measured from the Ozone Monitoring Instrument (OMI) have been used to detect enhancements and trends in NOₓ emissions due to gas flaring in high-latitude (up to 67°N) areas of Russia and North America (Li et al., 2016). Assessment of a suite of chemical transport models using OMI tropospheric NO₂ columns for summer 2008 showed a potential overestimate in NO₂ over biomass burning regions in eastern Siberia, with lower biases over European and North American source regions and underestimates over China (Emmons et al., 2015). A comparison of regional model-simulated tropospheric NO₂ columns with observations from OMI suggests potential underestimates in anthropogenic NO₂ emissions over high-latitude Siberia and the Russian Arctic (Thorp et al., 2021). Monks et al. (2015) exploited limited profile information from MOPITT CO retrievals to evaluate relationships between CO seasonal cycles in the lower and upper troposphere over the Arctic and midlatitude source regions. Atmospheric Infrared Sounder (AIRS) CO retrievals from 2007 to 2018 have been used to
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Map of the surface (red) and ozonesonde (blue) sites cited in the present study, with coordinates and elevation. Eureka and Alert are both surface and sounding sites. Utqiagvik was formerly called Barrow. The Arctic Circle at 66.55°N is also shown in the figure for reference.</figcaption></figure>

characterize atmospheric circulation patterns coincident with pollution enhancements during Arctic spring (Thomas et al., 2021), and IASI CO column measurements have been used to analyze transport pathways for Asian anthropogenic pollution to the Arctic (Ikeda et al., 2021). Osman et al. (2016) constructed three-dimensional (5° × 5°, 1 km) gridded climatologies of CO via a domain-filling trajectory mapping technique based on MOZAIC-IAGOS in situ measurements of commercial aircraft flights. These climatologies agreed well using forward and backward trajectories (< 10% difference for most cases) and against vertical measurements from MOZAIC-IAGOS not included in the climatologies. These climatologies were compared with CO retrievals from MOPITT; small biases were found in the lower troposphere, while differences of ~20% were found between 500 and 300 hPa, which declined throughout the study (2001-2012). Interannual variability in PAN retrieved by TES over eastern Siberia for April 2006–2008 was documented by Zhu et al. (2015), and it was shown to be largely controlled by boREAL fire emissions at this time of year. More recently, PAN data from the TES instrument were used to help characterize Asian influence on exported PAN and downwind O3 production (Jiang et al., 2016). A temperature-dependent high bias in TES PAN was found at cold temperatures over high latitudes.

In both Chapter 7 of the 2022 AMAP SLCF report (AMAP, 2022) and Whaley et al. (2022), data from satellite instruments, TES, the Atmosphere Chemistry Experiment (ACE) Fourier Transform Spectrometer (FTS) (ACE-FTS), and MOPITT are used to evaluate modeled O3, CH4, and CO in the Northern Hemisphere. They showed that model biases for CH4 were small, though they tended to be negative in the Arctic due to a lack of north-south gradient in the prescribed global distribution. Model biases were also negative for free-tropospheric O3; however, it was by approximately the same amount that TES O3 retrievals have been shown to be biased high by Verstraeten et al. (2013). The ACE-FTS comparison for O3 showed good agreement but had higher model biases around 300–100 hPa in Whaley et al. (2022) and AMAP (2022). The MOPITT CO comparisons in AMAP (2022) showed that all models’ CO values are biased low over land in the midlatitudes but biased high over the oceans at lower latitudes. Monks et al. (2015) discussed the fact that models had high biases in the outflow from Asia and low biases north of there due to lack of transport. The Quennehen et al. (2016) study also suggested that summer-time CO transport out of Asia is zonal. This could explain some of the underestimations in the Arctic CO in the mid-troposphere.

<table><thead><tr><th>Name</th><th>Longitude</th><th>Latitude</th><th>Elevation (m.s.l.)</th></tr></thead><tbody><tr><td colspan="4"><strong>Coastal, high Arctic:</strong></td></tr><tr><td>Alert</td><td>-62.33</td><td>82.50</td><td>66</td></tr><tr><td>Eureka</td><td>-86.4</td><td>80.1</td><td>83</td></tr><tr><td>Tiksi</td><td>128.9</td><td>71.6</td><td>10</td></tr><tr><td>Utqiagvik</td><td>-156.61</td><td>71.32</td><td>11</td></tr><tr><td>Villum</td><td>-16.67</td><td>81.60</td><td>20</td></tr><tr><td colspan="4"><strong>Near Arctic Circle:</strong></td></tr><tr><td>Denali</td><td>-151.19</td><td>63.11</td><td>660</td></tr><tr><td>Esrange</td><td>21.07</td><td>67.88</td><td>475</td></tr><tr><td>Karasjok</td><td>25.22</td><td>69.47</td><td>333</td></tr><tr><td>Pallas</td><td>24.25</td><td>67.97</td><td>565</td></tr><tr><td>Tustervatn</td><td>13.87</td><td>65.83</td><td>439</td></tr><tr><td colspan="4"><strong>High Arctic, high elevation:</strong></td></tr><tr><td>Zeppelin</td><td>11.89</td><td>78.91</td><td>474</td></tr><tr><td>Summit</td><td>-38.48</td><td>72.57</td><td>3211</td></tr><tr><td colspan="4"><strong>Ozone soundings only:</strong></td></tr><tr><td>Ny Ålesund</td><td>11.49</td><td>78.51</td><td></td></tr><tr><td>Resolute</td><td>-94.82</td><td>74.68</td><td></td></tr><tr><td>Scoresbysund</td><td>-21.97</td><td>70.48</td><td></td></tr><tr><td>Sodankyla</td><td>26.65</td><td>67.37</td><td></td></tr></tbody></table>
# 5 Arctic surface O₃ and precursors: seasonal cycles

In the high Arctic (> 70° N), there is very little diurnal variation in surface O₃ because the local and regional photochemistry is of limited importance most of the time and due to the 24 h daylight during Arctic spring, summer, and autumn as well as the polar night during winter. The lack of diurnal cycle is also because there is inefficient O₃ deposition to the ice, snow, and water surfaces in the Arctic and a sparsity of vegetation. Therefore, with less deposition and limited photochemical production, there is a very limited diurnal cycle. For high Arctic sites, the seasonal dynamics of O₃ can be explained mostly by long-range transport, particularly in the winter and springtime, and intrusion from aloft (Hirdman et al., 2010); see Figs. 1 and 5a. Moving southwards to the Polar Circle a clearer diurnal pattern is evident caused by the seasonal behavior of vertical mixing, deposition, transport, and local chemistry (Andersson et al., 2017; Aas et al., 2021; AMAP 2022) like the stations on the Scandinavian peninsula and Denali, central Alaska.

## 5.1 Surface ozone

Seasonal differences in the Arctic are important because of differences between the local meteorological conditions, as well as atmospheric transport, in the warm and the cold seasons and seasonal variations in O₃ sources and sinks. Surface O₃ at remote midlatitude sites with limited influence from local and regional anthropogenic O₃ precursor emissions has been found to frequently exhibit a characteristic seasonal cycle with peak values during spring and a minimum in the summer, while sites with high exposure to O₃ from anthropogenic precursors have summertime O₃ maxima (Monks, 2000; Parrish et al., 2013, 2019; Gaudel et al., 2018). The spring maxima have been explained by stratospheric intrusions as well as enhanced photochemical formation during this period of the year. The summer minima, e.g., observed at the Mace Head site (Derwent et al., 1998, 2013, 2020), which is strongly influenced by marine air, appear to be explained by photochemical destruction in the absence of anthropogenic precursors. Seasonal cycles at Arctic stations have been discussed in the literature, and it is evident that the halogen chemistry discussed above, which is most frequently observed at high Arctic coastal stations, leads to a significant reduction during the springtime (e.g., Oltman and Komhyr, 1986; Tarasick et al., 1995; Monks et al., 2015, Helmig et al., 2007). Anderson et al. (2017) found that monthly mean observed near-surface O₃ concentrations at background sites in Sweden from 1990 to 2013 had a maxima in spring, with the most northerly stations experiencing their maximum in April and the southerly (non-Arctic) ones in May.

In order to get an overview of the annual O₃ cycles at different types of Arctic surface measurement sites, we have calculated the monthly medians and interquartile range for the period 2003–2019 for a series of sites. A map of the sta-

tions as well as their coordinates and elevation can be seen in Fig. 4. Figure 5 illustrates the range of seasonal cycle behavior observed in the Arctic at different measurement sites and shows different seasonal cycles depending on location.

### 5.1.1 High Arctic sites

Figure 5a shows that the seasonalities in O₃ at Villum, Barrow (Utqiaġvik), Alert, Tiksi, and Eureka are similar: they have a local minimum in spring due to the occurrence of ODEs, a slight increase or recovery in June, and a second minimum in July due to surface removal and photochemical degradation of O₃. These stations are located at high-latitude coastal sites close to sea level. During winter, O₃ reaches a maximum; due to an absence of photochemical degradation of O₃, vertical mixing is suppressed during polar night since the Arctic boundary layer is often highly stratified, thus hampering removal by dry deposition (Esau and Sorokina, 2016).

### 5.1.2 Sites near the Arctic Circle

The characteristic seasonal variations of surface O₃ measured at stations close to the Arctic Circle are shown in Fig. 5b. The stations are Karasjok and Tustervatn in Norway (Aas et al., 2021), Esrange in Sweden, Pallas in Finland, and Denali in Alaska (note that regular O₃ monitoring at Karasjok ended in February 2010). The sites in Fig. 5b, which are not influenced by ODEs, exhibit a yearly cycle that is more similar to lower-latitude European stations at remote locations. Here, surface O₃ exhibits a late spring maximum which is attributed to photochemical production and transport of O₃ from the stratosphere (Monks, 2000). The largest differences between the stations are mainly found during the summer months, most likely due to differences in the influence of local sources on photochemical O₃ production (e.g., shipping; Marelle et al., 2016) and differences in the distance to pollution sources (Anderson et al., 2017).

### 5.1.3 Inland, high-elevation sites

Summit (located in the free troposphere on the Greenland Ice Sheet) is much less affected by bromine chemistry originating from sea ice or other low-altitude processes than the coastal high Arctic sites (Huang et al., 2017). Consequently, the seasonal variation is different with a clear maximum in May and a minimum in September; the higher concentrations compared to other surface stations can be explained by the high sensitivity to stratospheric O₃-enriched air (Monks et al., 2015) at this high-elevation (3211 m a.s.l.) site. Short episodes of depletion have been reported (Brooks et al., 2011), but they do not appear to substantially affect the monthly mean values as shown in Fig. 5c.

Zeppelin, although it is a high Arctic site, is located on a mountain ridge at 474 m a.s.l. and thus experiences free-tropospheric air masses more often compared to sea level
<figure><img src="image_9.png" /><figcaption>**Figure 5.** Seasonal behavior of surface O₃ at selected Arctic stations that are representative of sites in the (a) coastal high Arctic, (b) near the Arctic Circle, and (c) at high elevation. Monthly medians are calculated for the period 2003 to 2018. Data were not available from 2003 to 2006 for Villum or 2004 and 2013–2015 for Alert. Data from Tiksi were available for the period 2013–2018, and at Karasjok the measurements stopped in 2010. The error bars show upper (75%) and lower (25%) quartiles.</figcaption></figure>

sites. For this reason, it is less influenced by ODEs and consequently does not have an O₃ minimum in spring like the other high Arctic coastal stations (Fig. 5c). That said, ODEs have been reported there by Solberg et al. (1996), Lehrer et al. (1997), Berg et al. (2003), Eneroth et al. (2007), and Stefken et al. (2008), for example. ODEs have also been observed at the foot of the mountain, at the coastal station Gruvebadet, Ny-Ålesund (40 m a.s.l.), by Ianniello et al. (2021).

We also note that surface O₃ can be influenced by local anthropogenic emissions such as shipping (e.g., Marelle et al., 2016; Aliabadi et al., 2015; Eckhardt et al., 2013) or oil field emissions (McNamara et al., 2019). McNamara et al. (2019) discussed potentially important interactions between local anthropogenic NOₓ emissions from the Barrow (Utqiagvik) settlement or the Prudhoe Bay oil extraction facilities in northern Alaska and snowpack (chlorine) chemistry leading to elevated concentrations of nitrogen-containing compounds (e.g., N₂O₅, HO₂NO₂), with implications for Arctic tropospheric O₃. Therefore, while none of the Arctic sites

currently exhibit summertime surface maxima due to photochemical production, as often observed in polluted locations further south, this may change in the future with increasing local anthropogenic emissions (e.g., Granier et al., 2006; Law et al., 2014; Marelle et al., 2018).

He et al. (2016) measured O₃ and black carbon on a ship cruise to the Arctic Ocean (31.1 to 87.7°N and 9.3–90°E to 168.4°W) from June to September 2012. Comparing the observed O₃ concentrations to those measured at Barrow (Utqiagvik) showed no statistically significant differences; the authors suggest that coastal stations between July and September may be representative of the entire Arctic, but this hypothesis requires further investigation. Indeed, our results show significant differences in the O₃ seasonal cycles at different Arctic locations depending on whether they were coastal, inland, or high-elevation.
<figure><img src="image_10.png" /><figcaption>**Figure 6.** Arctic surface O₃ by month; seasonal cycle model comparisons. Top row: coastal high Arctic sites; middle row: sites near the Arctic Circle; bottom row: high-elevation sites. The solid black line is the observed O₃ monthly means, and the dashed black line is the multi-model median. Bottom row: sub-panels show the MMM percent difference [(MMM − measurements)/measurements × 100]. Note that model results are from the 2014–2015 mean. When available, the same years are used for the observations. However, Alert did not have data for 2014–2015, so its most recent years were used: 2010–2013. Summit had 2014 but only 1 month in 2015, so its 2013–2015 data were used.</figcaption></figure>

## 5.2 Surface O₃ model evaluation

It has been found that halogen chemistry, stable boundary layers, and dry deposition explained differences between measured and modeled O₃ concentrations, as demonstrated by Kanaya et al. (2019), who performed measurements of CO and O₃ during several ship cruises in the Bering Sea and the Arctic Ocean in September (2012 to 2017). None of the models in our study contain surface halogen chemistry, but they also display highly variable agreement in their surface O₃ seasonal cycles. Figure 6 shows the seasonal cycle from the models and observations averaged for 2014–2015 at several Arctic observation locations. Since the models do not contain surface-level bromine chemistry, at locations like Alert and Barrow (Utqiaġvik), they do not capture the springtime minimum in O₃. Some models (e.g., UKESM1) greatly underestimate wintertime O₃. This may be related to

deficiencies in boundary layer mixing or an overly shallow boundary layer depth, resulting in the overly active titration of O₃ by NO near NOₓ emission sources and subsequent underestimation of Arctic surface O₃. However, other model deficiencies could also play a role, including dry deposition and NOₓ lifetime. Indeed, Barten et al. (2021) found that overestimation of oceanic O₃ deposition can explain some differences between modeled and measured surface O₃ in the high Arctic. Some models in Fig. 6 do not agree on the timing of the springtime peak, with CMAM, DEHM, and GISS-E2.1 peaking in April and EMEP MSC-W and MRI-ESM2 peaking in May–June. The same groupings of models display different O₃ behavior at the end of the year (October–December), with CMAM, DEHM, and GISS-E2.1 all correctly simulating an increase in O₃ and EMEP-MSC-W and MRI-ESM2 having a decrease. All models agree bet-
ter with observations and each other on summertime surface O₃ abundance at all locations and on the full seasonal cycle at Summit, the high-elevation background location. The large range of modeled surface O₃ is similar to previous model studies (Shindell et al., 2008; Monks et al., 2015; Gaudel et al., 2018). Despite the large range in model performance, the overall average negative O₃ bias and the seasonality in model bias at Barrow (Utqiaġvik) and Summit are consistent with these previous studies. The comparisons highlight little change in the skill of models in simulating Arctic surface O₃ over the past decade.

These particular model simulations have been evaluated in Whaley et al. (2022), who grouped all western Arctic (defined as lat > 60° N, and long < 0°) and eastern Arctic (lat > 60° N, long > 0°) O₃ measurements together and showed the range in modeled and measured seasonal cycles for those two regions. That analysis included additional locations at lower latitudes, and thus their results emphasized that some models overestimated summertime O₃ in the western Arctic. Otherwise, the results from that study are consistent with what we report here.

## 5.3 Ozone precursors

NOₓ monitors have been used at several Arctic sites, but in a study at Zeppelin, it was shown that most of the NOₓ was in the form of the reservoir species PAN (Beine et al., 1997; Beine and Krognes, 2000). We evaluate and discuss PAN in Sect. 6.3 from aircraft measurements. There are only limited sources for NOₓ in the Arctic and the lifetime of NOₓ is on the order of a day. Whaley et al. (2022) evaluated surface NOₓ volume mixing ratios and found that these models underestimated surface NO₂ by -59% at low Arctic latitudes that were mostly around 60° N.

The dominant source for NOₓ is long-range transport of dominantly PAN (Beine and Krognes; 2000) and particulate-bound HNO₃, followed by reactivation in the Arctic by thermal decomposition and photoreduction processes, respectively. Kramer et al. (2015) determined at Summit from July 2008 to July 2010 that PAN accounted for 295 ppt and NOₓ for 88 ppt. In a more recent study, Huang et al. (2017) found in the period July 2008–June 2010 that PAN and NOₓ were maximum in spring at about 250 ppt and 25 ppt, respectively, and in summer 75 and 20 ppt, respectively. Beine and Krognes (2000) measured PAN at Zeppelin Mountain between 1994 and 1996. They found that 3-month seasonal mean values were lowest in summer at 89.4 ppt and highest in spring at 222.6 ppt. HNO₃ in the gas phase is in general very low (Wespes et al., 2012). Particulate-bound nitrate – potentially a significant source of NOₓ in the atmosphere and snowpack – is close to the detection limit in summer and up to 124.7 ng N m⁻³ in winter at Villum (Nguyen et al., 2013).

In general, non-methane VOC (NMVOC) concentrations in the Arctic are low, and thus their photo-oxidation has only a limited impact on O₃. There is a long-term measurement

study by Gautrois et al. (2003): studies that focus on long-range transport (Stohl, 2006; Harrigan et al., 2011), snow-pack emissions (Boudries et al., 2002; Dibb and Arsenault, 2002; Guimbaud et al., 2002; Barret et al., 2011; Gao et al., 2012), and shipborne measurements (Sjostedt et al., 2012 and Mungall et al., 2017). The Gautrois et al. (2003) study reported long-term VOC concentrations for Alert, NU; they found that yearly levels of ethane, propane, and toluene are 1.7, 0.6, and 26 pptv, respectively. For comparison, mixing ratios of ethane, propane, and toluene in China ranged from 3.7–17, 1.5–20.8, and 0.4–11.2 ppbv, respectively (Barletta et al., 2005).

Pernov et al. (2021) measured organic O₃ precursors online with a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) at Villum from April to October 2018. Sources were apportioned with positive matrix factorization. During the late spring, the Arctic haze factor was a source of oxygenated VOCs (OVOCs) arising from long-range transport of anthropogenic emissions, whilst during summer, OVOCs, namely organic acids, and dimethyl sulfide (DMS) originated from the marine cryosphere factor, with source regions in the Greenland Sea. During autumn, the biomass burning factor peaked in importance and was dominated by acetonitrile. The most abundant compound during the campaign was acetone, with a mean mixing ratio of 0.6 ppbv, as well as 0.027 ppbv for benzene and 0.046 ppbv for DMS. In the future, local NMVOC emissions might increase from both natural and anthropogenic sources due to retreating sea ice, with more biological activity, more industrial activity, and shipping affecting future levels of O₃. The long-term VOC measurements at Zeppelin and Pallas (Platt et al., 2022; Hellén et al., 2015) provide valuable datasets for better understanding tropospheric O₃ at those locations. However, in this study, models did not provide much VOC output and, when they did so, only as monthly means of a few species (e.g., ethane C₂H₆). Therefore, we did not evaluate modeled VOCs in this study other than CO.

Figure 7 shows the observed and simulated seasonal cycle of CO at Zeppelin and Barrow (Utqiaġvik). Simulated CO ranges about 50 ppbv across models, and all models underestimate surface CO at these sites. The low model biases are dominated by the winter and spring months. The 2014–2015 annual multi-model median (MMM) bias is -11% and -16% at Zeppelin and Barrow (Utqiaġvik), respectively. Figure 7 shows that for the first 6 months of the year, the MMM is 20%–30% too low, but that in the summer, the MMM is much closer to observations. These CO results are very similar to those found in previous multi-model studies (Shindell et al., 2008; Monks et al., 2015; Whaley et al., 2022). Similar to O₃, these results imply little change in the skill of models in simulating Arctic surface CO over the past decade. The modeled CO underestimations are well-reported in the literature and attributed to either a lack of CO from combustion sources in the emission inventories (e.g., Käsibhatla et al., 2002; Pétron et al., 2002; Jiang et al., 2015).
<figure><img src="image_11.png" /><figcaption>**Figure 7.** Arctic surface CO by month; seasonal cycle model comparisons. The solid black line represents the observed CO monthly means, and the dashed black line is the multi-model median (MMM). Bottom panels show the MMM percent difference [(MMM − measurements)/measurements × 100]. Note that model results are from the 2014–2015 mean. When available, the same years are used for the observations. However, for Zeppelin observations are the mean of 2013–2014.</figcaption></figure>

or to errors in OH, which impact the lifetime of CO (e.g., Monks et al., 2015; Quennehen et al., 2016). Indeed, both may be at cause here, as the anthropogenic CO emissions from ECLIPSEv6b are lower than those in the CMIP6 emission inventory, neither of which have taken into account the reported discrepancies from top-down emissions studies (Kasibhatla et al., 2002; Pétron et al., 2002; Jiang et al., 2015, Miyazaki et al., 2020). Monks et al. (2015) showed that models with lower global mean OH concentrations produced smaller underestimates in Arctic surface CO and that models with larger underestimates in CO over midlatitude source regions also had larger underestimates in Arctic CO. Emmons et al. (2015) showed that the models with larger tropospheric OH also had higher photolysis rates of O₃ to O(¹D) and that there was also some relationship between higher photolysis rates and lower cloud cover fraction in some models. Previous multi-model results have also shown that variability in model water vapor abundance in the Arctic appeared to be the leading driver of model variability in OH, despite being much less important at lower latitudes (Monks et al., 2015). Evaluating OH and water vapor is unfortunately beyond the scope of our study.

The models of this study prescribed CH₄ concentrations, including their increasing trend, and they were found to have a small bias of ~2% in Whaley et al. (2022) compared to surface and satellite measurements. Going forward, models are starting to simulate CH₄ explicitly from emissions, and

this will be important for simulating future changes in Arctic tropospheric chemistry.

## 6 Vertical distributions of O₃ and precursors in the Arctic

Observations and models have both demonstrated extensive layering of pollution signatures in the Arctic troposphere vertical profile, associated with varying air mass origins with altitude (Zheng et al., 2021; Willis et al., 2019). Large-scale isentropic transport pathways result in air masses from warmer more southerly latitudes being imported into the Arctic upper troposphere, while emissions from cooler northerly latitudes enter the Arctic near the surface and in the lower troposphere (Stohl, 2006). The presence of the Arctic dome during winter essentially shuts off access to the Arctic surface to air mass import from southerly midlatitudes, while it facilitates efficient low-level transport of emissions from northern Eurasia and Russia to the Arctic surface, giving rise to the well-known Arctic haze (Shaw, 1995). In practice, this large-scale dynamical control on long-range transport to the Arctic gives rise to a well-characterized vertical dependence of source region sensitivities for O₃ and precursors through the Arctic troposphere, where emissions from South and East Asia have the most influence in the Arctic upper troposphere, emissions from North America have the most influence in the Arctic mid-troposphere, and northern Eurasian and Russian emissions dominate at the surface (in addition to local influ-
ences) (Wespes et al., 2012; Monks et al., 2015). As mentioned in Sect. 1, this vertical layering and changes in the efficacy of O₃ radiative forcing with altitude have implications for the sensitivity of Arctic tropospheric O₃ forcing to regional emission perturbations.

Despite evidence for extensive vertical layering in the Arctic troposphere and the potential for highly varying source contributions with altitude, aside from a limited set of regular O₃ sonde profiles, there is a severe lack of observations available on the vertical distribution of O₃, and particularly its precursors, in the Arctic troposphere. There is an especially poor constraint on seasonal and interannual variability in O₃ precursor profiles. In this section, we make use of available vertical profile measurements of O₃ and its precursors to document our understanding of Arctic tropospheric O₃ profiles and to evaluate model-simulated vertical profiles of O₃ and precursors.

## 6.1 Ozonesondes

Ozone soundings provide a long-term record of Arctic O₃ through the depth of the troposphere. Since 1966, weekly soundings have been available at Resolute, and since the 1980s regular soundings, typically once a week, have been available from six stations north of 60°N (Fig. 4, Table S2). All of these stations are located in the Canadian and European sectors, meaning that regular soundings are lacking in a large sector of the Arctic (e.g., Russia and Alaska). The measurements are conducted using the balloon-borne electrochemical concentration cell (ECC) ozonesondes, typically reaching an altitude of about 30 km. Random uncertainties in tropospheric measurements are about 5%, and biases reported from field and laboratory comparisons to UV reference photometers are 1.0±4.4% in the lower troposphere and 5.3±4.4% in the upper troposphere (Tarasick et al., 2019b). Mean observed concentrations have a minimum close to the surface, gradually increase throughout the troposphere by about 50%, and then increase sharply going into the upper troposphere and lower stratosphere (Figs. 8 and S1–2 in the Supplement). Observed seasonal cycles in the Arctic troposphere generally show a maximum in spring and summer and a minimum in fall and winter. For example, Christiansen et al. (2017) examined long-term ozonesonde records at nine Arctic stations reporting consistent seasonal cycles as a function of altitude between sites with later maxima in the mid-troposphere compared to the surface layers and upper troposphere.

## 6.2 Model evaluation against ozonesondes

Figure 8 shows a comparison of the ozonesonde measurements at Eureka to the simulations from the 12 participating models for the annual and seasonal averages for the years 2014–2015. In the Supplement (Fig. S2), model-measurement comparisons at other Arctic locations are

shown. Generally, the models are highly variable, ranging ±50% of the measured O₃ profiles for most seasons and locations. The MMM performs well and is within ±8% throughout most of the troposphere. However, all models, except UKESM1, have a bulge with a high model bias around 300–400 hPa, which is at or near the tropopause, implying that most models simulate the tropopause height too low (having larger stratospheric O₃ concentrations appearing too low in altitude). This results in a positive bias of about 20% for the MMM around the tropopause. This feature in models was also reported in AMAP (2015), where model biases were particularly large at Ny-Ålesund and Summit. They associated those with differences in the transport of air masses from the stratosphere. This issue will have an impact on estimating the tropospheric O₃ burden, which is a common climate diagnostic (Griffiths et al., 2021).

At Alert, there are both surface and ozonesonde measurements, and we find that the results in the lowest levels of the Alert ozonesonde comparisons (Fig. S1) are consistent with the model biases found in Fig. 8 in that both show the models underestimating winter and fall O₃, overestimating spring, and matching observations well in the summer at this location.

Note that the models’ monthly average O₃ values were used in this comparison, which does not match the time of day and day of the week of the ozonesonde measurements. However, when a careful time matching to 3-hourly model output is carried out, the general features of the model biases remain the same (Fig. S2), likely because of the lack of a strong diurnal cycle in Arctic O₃ and its relatively long lifetime in the free troposphere.

The results of this model evaluation of the Arctic O₃ vertical profiles are consistent with Whaley et al. (2022), who compared the same model simulations to TES O₃ retrievals throughout the troposphere at lower Arctic locations (~60–70°N). They found models to be biased low (around –10%), though the TES measurements have been shown to be biased high by about the same amount (+13% bias in TES measurements reported in Verstraeten et al., 2013). They also saw a small positive shift in the model bias profile around 300 hPa. Finally, the Whaley et al. (2022) study included O₃, NOₓ, CH₄, and CO comparisons to the Atmosphere Chemistry Experiment (ACE) Fourier Transform Spectrometer (FTS) satellite instrument, and those results also independently implied that the modeled tropopause heights are too low.

## 6.3 Vertical distribution of O₃ precursors

Intensive field measurement campaigns using aircraft provide the most detailed observational constraint on vertical profiles of tropospheric O₃ precursors in the Arctic. While these datasets tend to provide excellent spatial and temporal resolution measurements on a wide range of species, they are episodic in nature and often cover only a period of a few days to several weeks, flying in specific regions of the
<figure><img src="image_12.png" /><figcaption>**Figure 8.** Comparison between observed (thick black line in the left panels) and AMAP models' (colored lines) $O_3$ seasonal averages for 2014–2015 at Eureka, NV, Canada. These use monthly mean model output. In each right panel, the dotted black line is the MMM, and the dashed black line shows zero bias for reference. See Fig. S1 for the rest of the ozonesonde locations and a sample comparison done with 3-hourly model output (Fig. S2).</figcaption></figure>

Arctic and often targeting specific layers or plumes. For example, Ancellet et al. (2016) examined aircraft, lidar, and ozonesonde data over Canada and Greenland during the summer of 2008 POLARCAT campaigns (Law et al., 2014). This study showed clear latitudinal and longitudinal variations in the origins of sampled air masses based on back trajectories and $O_3$ potential vorticity (PV) correlations. While downward transport of $O_3$ was important over Greenland, air masses with higher $O_3$ were attributed to North American boreal fires over Canada. Transport of polluted air masses from midlatitudes also contributed, for example from Asia north of 80°N.

The airborne NASA ATom (Atmospheric Tomography) mission (Wofsy et al., 2018; Thompson et al., 2022) has undertaken extensive surveying of the global troposphere. This includes repeated vertical profile measurements between 60 and 90°N providing useful insights into the variation of $O_3$ (Bourgeois et al., 2020) and its precursors through the depth of the Arctic troposphere at different times of the year. Figure 9 shows these mean results and their standard deviation

in the left-side panels, while the equivalent MMM results are in the right-side panels. The models' monthly mean results went into the MMM calculation, and the standard deviation from the models is shown.

The results show that near-surface $NO_2$ is greatly enhanced during winter, associated with a longer $NO_2$ lifetime and accumulation of pollution in the Arctic haze. The MMM simulates the surface $NO_2$ increase and the seasonality of the $NO_2$ profiles reasonably well. However, generally, the modeled $NO_2$ is biased low in the tropospheric profile, having average values of about 15 pptv in the 2–6 km range, whereas the measurements are about 25 pptv on average. This underestimate is consistent with that found at the surface by Whaley et al. (2022). PAN is also enhanced at the surface in the winter and can thermally decompose in the spring and summer to release $NO_x$. The MMM generally overestimates PAN (Fig. 9c–d) and does not simulate the same shape in vertical profiles. For example, models are not able to simulate the wintertime surface-level increase in PAN, and they have the inverse shape of the observed profile in April–May. The
<figure><img src="image_13.png" /><figcaption>**Figure 9.** Mean vertical profiles of O₃, CO, PAN, and NO₂ (left) measured in Alaska and Greenland from the NASA ATom missions during summer 2016, winter 2017, autumn 2017, and spring 2018 (horizontal lines indicate 1 standard deviation spread around mean values at each altitude). The (right) MMM for the years 2014–2015 (with the MMM standard deviation as horizontal lines). The observations appear as dashed lines in the right panels for ease of comparing to the MMM.</figcaption></figure>
best agreement is in summertime PAN (July–August), when the MMM vertical profile better matches that of the observations. The underestimate of NOₓ and the lack of winter surface increases in PAN by the models may be a reason why the wintertime surface O₃ concentrations in Sect. 5.2 and Fig. 5 were underestimated.

In line with ozonesonde data and previous airborne campaigns (AMAP, 2015), ATom profiles also demonstrate a springtime enhancement in O₃ extending through the troposphere, with evidence of stratospheric influence in the upper troposphere and lower O₃ in the summertime lower troposphere. The models capture that springtime O₃ enhancement as well. Summer enhancements in O₃ precursors, such as CO and PAN in the mid-troposphere, were also observed associated with the import of forest fire and anthropogenic emissions from lower latitudes, as also seen during POLARCAT in 2008. The models capture this feature for PAN, but less so for CO. Indeed, most models underestimate CO. The annual mean MMM bias for surface CO in the Northern Hemisphere has been reported to be -30% (Whaley et al., 2022). Figure 9 shows that below the tropopause, modeled O₃ is actually close to observed O₃, despite the significant MMM biases for CO, NOₓ, and PAN. Around the tropopause, the aircraft data show the same issue that the ozonesonde data did – that models significantly overestimate O₃ near the tropopause.

## 7 Conclusions

Recent research on Arctic tropospheric O₃ has resulted in improvements to our understanding of this pollutant and GHG in the rapidly changing and sensitive Arctic environment. We have shown in this study that Arctic surface O₃ seasonal cycles are different depending on whether sites are near the coast, inland, or at high elevation. Coastal sites have springtime minima due to halogen chemistry causing ODEs and show a maximum during the winter. The inland locations near the Arctic Circle have quite consistent seasonal cycles, with maxima in April and minima in August. While the high-elevation sites that are less influenced by halogen chemistry than coastal locations are more variable, Summit has a later maximum (May) and minimum (September); Zeppelin has an earlier maximum (March) and minimum (July).

Despite model development that has occurred since the 2015 AMAP assessment report on ozone (AMAP, 2015) to add processes, improve parameterizations, and increase resolution, among others, the resulting performance of the models remains more or less the same in terms of model variability and biases compared to measured O₃ and O₃ precursor species in the Arctic. Model results for CO would improve if CO emissions from combustion were increased, as suggested in the literature. It would also be useful to compare modeled OH and VOCs in the Arctic, but that was beyond the scope of this study. However, as Arctic O₃ is limited by NOₓ availability, improvements to CO and VOCs may not have a large

effect on O₃. Improvements to modeled PAN and NOₓ are needed; however, sensitivity studies to determine the cause of the model biases will be required to improve model performance for those species. For surface O₃ distributions in the Arctic, models simulate background levels reasonably well (e.g., at the high-elevation location of Summit), but surface bromine–halogen chemistry needs to be included to simulate springtime surface O₃ properly in the Arctic. Except near the tropopause, models simulate O₃ throughout the vertical profile well, with the MMM performing best at ±8% depending on the location and altitude in the troposphere. Attention to improving the height of the modeled tropopause and/or the stratosphere–tropospheric exchange is still required since downward transport of high stratospheric O₃ concentrations is causing model biases around 6 to 8 km (400 to 300 hPa) to be significantly large (> 20%).

While they are logistically challenging, additional O₃ measurements in the Arctic, such as O₃ deposition measurements, observations of stratospheric–tropospheric exchange, and O₃ concentrations in the Siberian Arctic, together with long-term measurements of O₃ precursors (such as those performed at Zeppelin and Pallas), would be particularly helpful to improve our understanding and modeling capabilities. This is particularly important as climate change alters the chemistry and dynamics of tropospheric O₃ in the future.

**Code and data availability.** The surface monitoring datasets are available online. WDCGG for CH₄: https://gaw.kishou.go.jp/login/user (last access: 14 April 2022, Global Atmosphere Watch, 2022). EBAS for European (EMEP) and several Arctic locations: http://ebas-nilu.no/ (last access: 14 April 2022, Norwegian Institute for Air Research, 2022). NAPS: https://open.canada.ca/data/en/dataset/1b36a356-defd-4813-acea-47bc3abd859b (last access: 14 April 2022, Environment and Climate Change Canada, 2022). The ozonesonde data were obtained from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) at https://woudc.org and from the Network for Detection of Atmospheric Composition Change (NDACC) at https://www.ndacc.org (last access: 4 January 2023, NDACC, 2023). The model output files in NetCDF from the simulations used in this project can be found here: https://open.canada.ca/data/en/dataset/c9a333ea-b81c-4df3-9880-ea7c3daeb76f (last access: 4 January 2023, ECCC, 2023). Some of the model codes are available online at the following locations. CESM2: https://www.cesm.ucar.edu/models/cesm2/ (last access: 14 April 2022, UCAR, 2022). GEOS-Chem: http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_12{#}12.3.2 (last access: 14 April 2022, Harvard University, 2022). GISS-E2.1: https://www.giss.nasa.gov/tools/modelE/ (last access: 14 April 2022, NASA, 2022). Oslo CTM: https://github.com/NordicESMhub/OsloCTM3 (last access: 14 April 2022, Section for Meteorology and Oceanography, 2022). The other model codes may be available upon request.

**Supplement.** The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-637-2023-supplement.
**Author contributions.** CHW, KSL, JLH, HS, SRA, JL, and JBP wrote the paper and created Figs. 3–9. RYC, JF, and XD provided the GEOS-Chem model output. JF, ST, and DT edited and provided comments on the paper. JHC provided the DEHM model output. GF, UI, and KT provided the GISS-E2.1 model output. MG and ST provided the EMEP MSC-W model output. KSL, JCR, TO, and LM provided the WRF-Chem model output. MD and NO provided the MRI-ESM2 model output. DAP provided the CMAM model output. LP provided the CESM model output. RS provided the OsloCTM model output. MAT provided the MATCH-SALSA model output. SRA and STT provided the UKESM1 model output. DT provided the Canadian ozonesonde measurements, and RK provided Finnish ozonesonde measurements. MF and KvS provided the model strategy for this project. PE and IP provided the Summit and Barrow datasets, and SS provided the Karasjok and Tustervatn datasets. GB, GH, IB, TR, JP, and CT provided the ATom datasets.

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of *Atmospheric Chemistry and Physics*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Acknowledgements.** We wish to acknowledge Siyuan Wang and Kerri A. Pratt for their figure originally published in PNAS, as well as Jeffrey Seabrook and James Whiteway for their figure originally published in the *Journal of Geophysical Research*. We thank the ATom team for the original aircraft measurements. The technicians and logistical support staff at the different stations are gratefully acknowledged for their work, particularly Doug Worthy for Alert data, Karin Sjöberg for the Esrange data, and Karri Saarnio for the Pallas data.

**Financial support.** Makoto Deushi and Naga Oshima were supported by the Japan Society for the Promotion of Science KAK-ENHI (grant numbers: JP18H03363, JP18H05292, JP19K12312, JP20K04070 and JP21H03582), the Environment Research and Technology Development Fund (JPMEERF20202003 and JP-MEERF20205001) of the Environmental Restoration and Conservation Agency of Japan, the Arctic Challenge for Sustainability II (ArCS II) under program grant number JPMXD1420318865, and a grant for the Global Environmental Research Coordination System from the Ministry of the Environment, Japan (MLIT1753 and MLIT2253). Joakim Langner and Manu A. Thomas were supported by the Swedish Environmental Protection Agency through contract NV-03174-20 and the Swedish Clean Air and Climate research program. Svetlana Tsyro and Michael Gauss have received support from the AMAP Secretariat and the EMEP Trust Fund. Ulas Im received support from the Aarhus University Interdisciplinary Centre for Climate Change (iClimate) OH fund (no. 2020-0162731), the FREYA project funded by the Nordic Council of Ministers (grant agreement nos. MST-227-00036 and MFVM-

2019-13476), and the EVAM-SLCF funded by the Danish Environmental Agency (grant agreement no. MST-112-00298). Henrik Skov received funding from the Danish Ministry for Energy, Climate and Utilities (grant agreement no. 2018-3767), the Danish Environmental Agency (grant agreement no. MST-113-00140), and AMA. Kostas Tsigaridis and Gregory Faluvegi received support from the NASA Modeling, Analysis and Prediction Program (MAP). Steven T. Turnock received financial support from the Arctic Monitoring and Assessment Programme. Kathy S. Law, Jean-Christophe Raut, Louis Marelle, and Tatsuo Onishi (LATMOS) received support from the EU iCUPE (Integrating and Comprehensive Understanding on Polar Environments) project (grant agreement no. 689443) under the European Network for Observing our Changing Planet (ERA-Planet) and from access to IDRIS HPC resources (GENCI allocation A009017141) as well as the IPSL mesoscale computing center (CICLAD: Calcul Intensif pour le Climat, l'Atmosphère et la Dynamique) for model simulations. Jesper Christensen (DEHM model) received support from the Danish Environmental Protection Agency and Danish Energy Agency (DANCEA funds for Environmental Support to the Arctic Region project: grant no. 2019-7975, grant no. MST-112-00298, grant no. TAS 4005-0153). Stephen R. Arnold and Steven T. Turnock both received financial support from the Arctic Monitoring and Assessment Programme. Stephen R. Arnold also received support from the UK Natural Environment Research Council and Belmont Forum via the ACRoBEAR project (grant NE/T013672/1). Joshua Fu received funding from the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC05-00OR22725. The research by Irina Petropavlovskikh and Peter Effertz was supported by NOAA cooperative agreements NA17OAR4320101 and NA22OAR4320151.

**Review statement.** This paper was edited by Radovan Krejci and reviewed by two anonymous referees.

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