We present updated evaluation of stratospheric ozone profile trends in the 60 degrees S-60 degrees N latitude range using long-term ground-based and satellite climate data records, as well as simulations by chemistry-climate models. The trends are evaluated using the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) regression model.Analyses of satellite data confirm the statistically significant positive ozone trends in the period 2000-2024 in the upper stratosphere of similar to 1-3 % per decade, with larger trends at mid-latitudes compared to the tropics. The trends are slightly positive or close to zero in the middle stratosphere, and mostly negative, -1 to -2 % per decade, in the lower stratosphere, but they are not statistically significant. The morphology and magnitude of ozone trends are similar to previous analyses (2000-2020 trends).Ozone trends in 2000-2024 predicted by chemistry-climate model simulations are in good agreement with combined satellite trends. In the upper stratosphere, models predict a slightly stronger ozone recovery than observations. In the lower stratosphere, both models and satellite observations report negative trends in the tropics, while modelled ozone trends are slightly positive at mid-latitudes.Ozone profile trends over several stations estimated from ground-based records capture the same overall vertical pattern of ozone trends as merged gridded satellite datasets.Analyses of regional ozone profile trends in 2003-2024 using merged satellite datasets confirmed the previous observations of a longitudinal structure in ozone trends in the NH mid-latitude stratosphere, with positive trends over Scandinavia and negative trends over Siberia. However, the magnitude of this dipole-like structure is reduced compared to previous analyses.

Contrary to the Antarctic, where ozone recovery has been observed for about a decade, the detection of positive ozone trends in the Arctic remains challenging due to higher natural variability of ozone in that region.Using a merging of long-term ozone data from Fourier transform infrared spectrometers, ozonesondes, and Dobson and Brewer spectrophotometers, we present regional long-term trends (2000-2024) for total, stratospheric and tropospheric ozone. First, ground-based measurements are cross-compared to two satellite data sets (MEGRIDOP and IASI-CDR). This enables the detection of drifts in ground-based data sets we further exclude from our study. We then use a representativeness study based on CAMS re-analysis data to define regions for which representative trends with reduced uncertainties are obtained by combining data sets from different instruments and stations. Annual and seasonal trends are calculated using a multiple linear regression technique involving a set of proxies that represent physical processes influencing the natural ozone variability.Annual trends indicate increasing total ozone over the Arctic, and are statistically significant over Canada and Reykjavik (+2.1 % per decade) and North-West Europe (Harestua and Lerwick, +0.7 % per decade). Ozone recovery is also observed over Canada in the mid-stratosphere (+2.0 % per decade) and over the North Pole region (Canada and Ny-& Aring;lesund) in the upper stratosphere (+2.1 % per decade to +3.8 % per decade). By analyzing the sensitivity of the ozone trends to the proxies, we observe a slow down of the expected ozone recovery, especially in the lower stratosphere, due to stratospheric cooling (-0.6 % per decade) and to the increase of volume of polar stratospheric clouds (-0.8 % per decade).

This study presents an updated evaluation of stratospheric ozone profile trends at Arosa/Davos/Hohenpeißenberg, Switzerland/Germany; Observatory de Haute-Provence (OHP), France; Boulder, Colorado, Mauna Loa Observatory (MLO) and Hilo, Hawaii; and Lauder, Aotearoa / New Zealand, with a focus on the ozone recovery period post-2000. Trends are derived using vertical ozone profiles from NOAA's Dobson network via the Umkehr method (with a recent new homogenization), ozonesondes, and the NOAA COHesive Solar Backscatter Ultraviolet Instrument (SBUV)/Ozone Mapping and Profiler Suite (OMPS) satellite-based record (COH) sampled to match the geographical coordinates of the ground-based stations used in this study. Analyses of long-term changes in stratospheric ozone time series were performed using the updated version (0.8.0) of the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) independent linear trend (ILT) regression model. This study finds consistency between the trends derived from the different observational records, which is a key factor to the understanding of the recovery of the ozone layer after the implementation of the Montreal Protocol and its amendments that control ozone-depleting substance production and release into the atmosphere. The northern hemispheric Umkehr records of Arosa/Davos, OHP, and MLO all show positive trends in the mid- to upper stratosphere, with trends peaking at ∼ +2 % per decade. Although the upper-stratospheric ozone trends derived from COH satellite records are more positive than those detected by the Umkehr system, the agreement is within the 2 times the standard error uncertainty. Umkehr trends in the upper stratosphere at Boulder and Lauder are positive but not statistically significant, while COH trends are larger and statistically significant (within 2 times the standard error uncertainty). In the lower stratosphere, trends derived from Umkehr and ozonesonde records are mostly negative (except for positive ozonesonde trends at OHP); however, the uncertainties are quite large. Additional dynamical proxies were investigated in the LOTUS model at five ground-based sites. The use of additional proxies did not significantly change trends, but the equivalent latitude reduced the uncertainty in the Umkehr and COH trends in the upper stratosphere and at higher latitudes. In lower layers, additional predictors (tropopause pressure for all stations; two extra components of Quasi-Biennial Oscillation at MLO; Arctic Oscillation at Arosa/Davos, OHP, and MLO) improve the model fit and reduce trend uncertainties as seen by Umkehr and sonde.

Previous assessments on modelling Arctic tropospheric ozone (O3) have shown that most atmospheric models continue to experience difficulties in simulating tropospheric O3 in the Arctic, particularly in capturing the seasonal variations at coastal sites, primarily attributed to the lack of representation of surface bromine chemistry in the Arctic. In this study, two independent chemical transport models (CTMs), DEHM (Danish Eulerian Hemispheric Model) and GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and Chemistry), were used to simulate Arctic lower-tropospheric O3 for the year 2015 at considerably higher horizontal resolutions (25 and 15 km, respectively) than the large-scale models in the previous assessments. Both models include bromine chemistry but with different mechanistic representations of bromine sources from snow- and ice-covered polar regions: a blowing-snow bromine source mechanism in DEHM and a snowpack bromine source mechanism in GEM-MACH. Model results were compared with a suite of observations in the Arctic, including hourly observations from surface sites and mobile platforms (buoys and ships) and ozonesonde profiles, to evaluate models' ability to simulate Arctic lower-tropospheric O3, particularly in capturing the seasonal variations and the key processes controlling these variations. Both models are found to behave quite similarly outside the spring period and are able to capture the observed overall surface O3 seasonal cycle and synoptic-scale variabilities, as well as the O3 vertical profiles in the Arctic. GEM-MACH (with the snowpack bromine source mechanism) was able to simulate most of the observed springtime ozone depletion events (ODEs) at the coastal and buoy sites well, while DEHM (with the blowing-snow bromine source mechanism) simulated much fewer ODEs. The present study demonstrates that the springtime O3 depletion process plays a central role in driving the surface O3 seasonal cycle in central Arctic, and that the bromine-mediated ODEs, while occurring most notably within the lowest few hundred metres of air above the Arctic Ocean, can induce a 5 %–7 % of loss in the total pan-Arctic tropospheric O3 burden during springtime. The model simulations also showed an overall enhancement in the pan-Arctic O3 concentration due to northern boreal wildfire emissions in summer 2015; the enhancement is more significant at higher altitudes. Higher O3 excess ratios (ΔO3/ΔCO) found aloft compared to near the surface indicate greater photochemical O3 production efficiency at higher altitudes in fire-impacted air masses. The model simulations further indicated an enhancement in NOy in the Arctic due to wildfires; a large portion of NOy produced from the wildfire emissions is found in the form of PAN that is transported to the Arctic, particularly at higher altitudes, potentially contributing to O3 production there.

Tropospheric ozone trends from models and satellites are found to diverge. Ground-based (GB) observations are used to reference models and satellites, but GB data themselves might display station biases and discontinuities. Reprocessing with uniform procedures, the TOAR-II working group Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements (HEGIFTOM) homogenized public data from five networks: ozonesondes, In-service Aircraft for a Global Observing System (IAGOS) profiles, solar absorption Fourier transform infrared (FTIR) spectrometer measurements, lidar observations, and Dobson Umkehr data. Amounts and uncertainties for total tropospheric ozone (TrOC; surface to 300 hPa), as well as free- and lower-tropospheric ozone, are calculated for each network. We report trends (2000 to 2022) for these segments using quantile regression (QR) and multiple linear regression (MLR) for 55 datasets, including six multi-instrument stations. The findings are that (1) median TrOC trends computed with QR and MLR trends are essentially the same; (2) pole-to-pole, across all longitudes, TrOC trends fall within +3 to −3 ppbv per decade, equivalent to (−4 % to +8 %) per decade depending on site; (3) the greatest fractional increases occur over most tropical and subtropical sites, with decreases at northern high latitudes, but these patterns are not uniform; (4) post-COVID trends are smaller than pre-COVID trends for Northern Hemisphere mid-latitude sites. In summary, this analysis conducted in the frame of TOAR-II/HEGIFTOM shows that high-quality, multi-instrument, harmonized data over a wide range of ground sites provide clear standard references for TOAR-II models and evolving tropospheric ozone satellite products for 2000–2022.

Quantifying long-term free-tropospheric ozone trends is essential for understanding the impact of human activities and climate change on atmospheric chemistry. However, this is complicated by two key challenges: the differences among existing satellite-derived tropospheric ozone products, which are not yet fully understood or reconciled, and the limited temporal and spatial coverage of ground-based reference measurements. Here, we explore if a more consistent understanding of the geographical distribution of tropospheric ozone column (TrOC) trends can be obtained by focusing on regional trends from ground-based measurements. Regions were determined with a correlation analysis between modeled TrOCs at the site locations. For those regions, TrOC trends were estimated with quantile regression for the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) and with a linear mixed-effects modeling (LMM) approach to calculate synthesized trends from homogenized HEGIFTOM (Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements) individual site trends. For different periods (1990-2021/22, 1995-2021/22, 2000-2021/22), both approaches give increasing (partial) tropospheric ozone column amounts over almost all Asian regions (median confidence) and negative trends over Arctic regions (very high confidence). Trends over Europe and North America are mostly weakly positive (LMM) or negative (TOST). For both approaches, the 2000-2021/22 trends decreased in magnitude compared to 1995-2021/22 for most regions; and for all time periods and regions, the pre-COVID trends are larger than the post-COVID trends. Our results enable the validation of global satellite TrOC trends and assessment of the performance of atmospheric chemistry models to represent the distribution and variation of TrOC.

This study explores the role of snowpack in polar boundary layer chemistry, especially as a direct source of reactive bromine (BrOx = BrO + Br) and nitrogen (NOx = NO + NO2) in the Arctic springtime. Surface snow samples were collected daily from a Canadian high Arctic location at Eureka, Nunavut (80° N, 86° W) from the end of February to the end of March in 2018 and 2019. The snow was sampled at several sites representing distinct environments: sea ice, inland close to sea level, and a hilltop ∼ 600 m above sea level (a.s.l.). At the inland sites, surface snow salinity has a double-peak distribution with the first and lowest peak at 0.001–0.002 practical salinity unit (psu), which corresponds to the precipitation effect, and the second peak at 0.01–0.04 psu, which is likely related to the salt accumulation effect (due to loss of water vapour by sublimation). Snow salinity on sea ice has a triple-peak distribution; its first and second peaks overlap with the inland peaks, and the third peak at 0.2–0.4 psu is likely due to the sea water effect (a result of upward migration of brine). At all sites, snow sodium and chloride concentrations increase by almost 10-fold from the top 0.2 to ∼ 1.5 cm. Surface snow bromide at sea level is significantly enriched, indicating a net sink of atmospheric bromine. Moreover, surface snow bromide at sea level has an increasing trend over the measurement period, with mean slopes of 0.024 µM d−1 in the 0–0.2 cm layer and 0.016 µM d−1 in the 0.2–0.5 cm layer. Surface snow nitrate at sea level also shows a significant increasing trend, with mean slopes of 0.27, 0.20, and 0.07 µM d−1 in the top 0.2, 0.2–0.5, and 0.5–1.5 cm layers, respectively. Using these trends, an integrated net deposition flux of bromide of (1.01 ± 0.48) × 107 molec.cm-2s-1 and an integrated net deposition flux of nitrate of (2.6 ± 0.37) × 108 molec.cm-2s-1 were derived. In addition, the surface snow nitrate and bromide at inland sites were found to be significantly correlated (R = 0.48–0.76) with the [NO3-]/[Br-] ratio of 4–7 indicating a possible acceleration effect of reactive bromine in atmospheric NOx-to-nitrate conversion. This is the first time such an effect has been seen in snow chemistry data obtained with a sampling frequency as short as 1 d. BrO partial column (0–4 km) data measured by MAX-DOAS show a decreasing trend in early spring, which generally agrees with the derived surface snow bromide deposition flux indicating that bromine in Eureka atmosphere and surface snow did not reach a photochemical equilibrium state. Through mass balance analysis, we conclude that the average release flux of reactive bromine from snow over the campaign period must be smaller than the derived bromide deposition flux of ∼ 1 × 107 molec.cm-2s-1. Note that the net mean fluxes observed do not completely rule out larger bidirectional fluxes over shorter timescales.

High-quality long-term observational records are essential to ensure appropriate and reliable trend detection of tropospheric ozone. However, the necessity of maintaining high sampling frequency, in addition to continuity, is often under-appreciated. A common assumption is that, so long as long-term records (e.g., a span of a few decades) are available, (1) the estimated trends are accurate and precise, and (2) the impact of small-scale variability (e.g., weather) can be eliminated. In this study, we show that the undercoverage bias (e.g., a type of sampling error resulting from statistical inference based on sparse or insufficient samples, such as once-per-week sampling frequency) can persistently reduce the trend accuracy of free tropospheric ozone, even if multi-decadal time series are considered. We use over 40 years of nighttime ozone observations measured at Mauna Loa, Hawaii (representative of the lower free troposphere), to make this demonstration and quantify the bias in monthly means and trends under different sampling strategies. We also show that short-term meteorological variability remains a cause of an inflated long-term trend uncertainty. To improve the trend precision and accuracy due to sampling bias, two remedies are proposed: (1) a data variability attribution of colocated meteorological influence can efficiently reduce estimation uncertainty and moderately reduce the impact of sparse sampling, and (2) an adaptive sampling strategy based on anomaly detection enables us to greatly reduce the sampling bias and produce more accurate trends using fewer samples compared to an intense regular sampling strategy.

Long-term, 21st century ground-based ozone measurements are crucial to study the recovery of stratospheric ozone as well as the trends of tropospheric ozone. This study is performed in the context of the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) and TOAR-II (Tropospheric Ozone Assessment Report, phase II) initiatives. Within LOTUS, we want to know why different trends have been observed by different ground-based measurements at Lauder. In TOAR-II, intercomparison studies among the different ground-based datasets are needed to evaluate their quality and relevance for trend studies. To achieve these goals, we perform an intercomparison study of total column ozone and its vertical distribution among the ground-based measurement instruments available at the Lauder station from 2000 to 2022, which are a Fourier transform infrared (FTIR) spectrometer, a Dobson spectrophotometer, a UV2 (ultraviolet double monochromator), a microwave radiometer (MWR), ozonesondes, and a stratospheric lidar. Because only the latter two provide high-vertical-resolution profiles, the vertical ozone distribution is validated using partial columns, defined to provide independent information: one tropospheric column and three stratospheric columns. Because FTIR provides total columns and vertical information covering all partial columns as well as high temporal sampling, the intercomparisons (bias, scatter, and drift) are analyzed using FTIR as the reference.Very good agreement between the FTIR and Dobson (FTIR and UV2) total column ozone records is apparent in the high Pearson correlation of 0.97 (0.93), low biases of -3 % (-2 %), and 2 % (3 %) dispersions, which are within the respective systematic and random uncertainties. The small observed drifts 0.4 % (0.3 %) per decade are "non-significant" (or rather a low certainty in a 95 % confidence interval) and show good stability of the three ozone total column series at Lauder.In the troposphere we find a small bias of -1.9 % with the ozonesondes but a larger one (+10.7 %) with Umkehr, which can be explained by the low degrees of freedom for signal (0.5) of Umkehr in the troposphere. However, no significant drift is found among the three instruments in the troposphere, which proves their relevance for trend studies within TOAR-II. The negative bias observed in total columns is confirmed by negative biases in all stratospheric columns for all instruments with respect to FTIR (between -1.2 % and -6.8 %). This, confirmed by the total column biases, points to a 2 %-3 % underestimation of the infrared spectroscopic line intensities. Nevertheless, the dispersion between FTIR and all techniques is typically within 5 % for the stratospheric partial columns, in close agreement with the given random uncertainty budgets.We observe no significant drift in the stratosphere between ozonesondes and FTIR for all partial columns, with ozonesonde trends being less negative than in LOTUS further referred to as the LOTUS22. The only significant drift in the lower-stratospheric columns is obtained between FTIR and Umkehr, as was already found in LOTUS22. Two significant positive drifts are observed in the middle stratosphere (2 % and 3 % per decade) with lidar and MWR, respectively, while two significant negative drifts are observed in the upper stratosphere (-3 % and -4 % per decade) with Umkehr and lidar, respectively. While remaining drifts are still present, our study explains roughly half of the differences in observed trends in LOTUS22 by the different sampling, vertical sensitivity, or time periods and gaps. In addition, the FTIR data in the present work have been improved since LOTUS22, reducing the differences in the upper-stratospheric and tropospheric trends. This shows the necessity for continuous review and improvement of the measurement and retrieval processes. This study also reflects the importance of super sites such as Lauder for cross-validating the long-term ozone measurements. Our study demonstrated that well-harmonized, optimized, well-characterized instruments that show very good agreement in terms of bias, dispersion, and correlation are capable of detecting trends that agree within their respective measurement uncertainties.

This study has produced an improved percentile and seasonal (median) trend estimate of free tropospheric ozone above western North America (WNA), through a data fusion of ozonesonde, lidar, commercial aircraft, and field campaign measurements. Our method combines heterogeneous data sets according to the consensus data characteristics and inherent uncertainty in order to produce our best fused product. In response to different data collection environments (in situ or ground-based), we investigate the ozone variability based on a wide range of percentiles, which is preferable for trend detection due to tropospheric ozone's high degree of heteroscedasticity (i.e., inconsistent trends and variability between different ozone percentiles). We then compare the ozone trends and variability above the California sub-domain to the full WNA region for better understanding of the correlations between different regional scales. In California, the 1995-2021 percentile (from the 5th to 95th) and seasonal trends are clearly positive in terms of high signal-to-noise ratios. The magnitude of the trends is generally weaker over WNA compared to California, but reliable positive trends can still be found between the 10th and 70th percentiles, as well as winter and summer, whereas autumn shows a negative trend over the same period. In addition, dozens of rural surface sites across the region are selected to represent the boundary layer variability. In contrast to increasing free tropospheric ozone, we find overall strong negative surface trends since 1995, with the greatest divergence found in summer. Throughout the analysis implications of the COVID-19 economic downturn on ozone variability are discussed. Plain Language Summary Free tropospheric ozone above western North America has increased since the mid-1990s. Despite an observed drop of ozone in 2020 due to the COVID-19 economic downturn, this observation-based study shows the overall free tropospheric ozone trends have not been offset and continued to increase over 1995-2021, mainly driven by strong positive trends in winter and summer. In combination with the strong negative trends observed at rural surface sites over the same period, this study adds to the growing body of evidence that surface trends are frequently disconnected from the general increases observed in the free troposphere.

Observed trends in tropospheric ozone, an important air pollutant and short-lived climate forcer (SLCF), are estimated using available surface and ozonesonde profile data for 1993-2019, using a coherent methodology, and compared to modeled trends (1995-2015) from the Arctic Monitoring Assessment Program SLCF 2021 assessment. Increases in observed surface ozone at Arctic coastal sites, notably during winter, and concurrent decreasing trends in surface carbon monoxide, are generally captured by multi-model median trends. Wintertime increases are also estimated in the free troposphere at most Arctic sites, with decreases during spring months. Winter trends tend to be overestimated by the multi-model medians. Springtime surface ozone increases in northern coastal Alaska are not simulated while negative springtime trends in northern Scandinavia are not always reproduced. Possible reasons for observed changes and model performance are discussed including decreasing precursor emissions, changing ozone dry deposition, and variability in large-scale meteorology. The Arctic is warming much faster than the rest of the globe due to increases in carbon dioxide, and other trace constituents like ozone, also an air pollutant. However, improved understanding is needed about long-term changes or trends in Arctic tropospheric ozone. A coherent methodology is used to identify trends in surface and regular profile measurements over the last 20-30 years, and results from six chemistry-climate models. Increases in observed ozone are found at the surface and in the free troposphere during winter in the high Arctic. Paradoxically, decreases in nitrogen oxide emissions at mid-latitudes appear to be leading to increases in ozone during winter, but associated increases in Arctic tropospheric ozone tend to be overestimated in the models. Increases are also found at the surface in northern Alaska during spring but not reproduced by the models. The causes are unknown but could be related to changes in local sources or sinks of Arctic ozone or in large-scale weather patterns. Declining mid-latitude emissions, or increased dry deposition to northern forests, may explain negative surface ozone trends over northern Scandinavia in spring that are not always captured by the models. Further work is needed to understand changes in Arctic tropospheric ozone. Coherent ozone trend analysis methodology applied to multi-decade, pan-Arctic surface and ozonesonde datasets and multi-model mediansIncreasing winter Arctic tropospheric ozone overestimated by models in the free troposphere, and spring surface changes not capturedSpring (summer) decreases (increases) in observed ozone throughout the troposphere, not always simulated by models

Ground-based observations are indispensable for the long-term monitoring of atmospheric constituents. In this work, we take advantage of multiple collocated instruments to analyze potential biases and drifts in ground-based ozone observations, within the context of the HEGIFTOM (Harmonization and Evaluation of Ground-based Instruments for Free Tropospheric Ozone Measurements) working group in the Tropospheric Ozone Assessment Report, Phase II (TOAR-II). This work is performed at Lauder (New Zealand), Boulder (Colorado), and Mauna Loa (Hawaii) where comparisons are made between Fourier Transform Infrared (FTIR) spectroscopy, Dobson Umkehr, and ozonesonde observations. The validation is performed with respect to FTIR as arbitrary reference, while considering the differences between the a priori profile information for the techniques that employ these as well as accounting for the different vertical resolution of each measurement technique. Such intercomparison is done for a handful discrete altitude partial columns, defined to have independent pieces of information also in the case of the low vertical resolution techniques (FTIR and Umkehr). This leads to 4 independent vertical layers to be compared, including one in the troposphere where ozone plays an important role as a greenhouse gas and as a risk to human health. In this tropospheric layer we compare the FTIR, Dobson Umkehr and ozonesonde techniques and derive a consistent bias from the FTIR data of about 5%, which we attribute in part to the assumed spectroscopy. Fitting the time series of the relative measurement differences using multiple linear regression, we obtain a linear trend, which quantifies the drift between pairs of techniques. Within the uncertainties, we find no significant drift between FTIR and Umkehr or ozone sonde data in the troposphere.

With a few exceptions, most studies on tropospheric ozone (O 3 ) variability during and following the COrona VIrus Disease (COVID-19) economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O 3 at several high-elevation sites across North America and western Europe. Monthly O 3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000-2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in western Europe, 19 rural sites in the western US, 4 sites in the western US downwind of highly polluted source regions, and 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a "global" reference. In 2020, the European high-elevation sites showed persistent negative surface O 3 anomalies during spring (March-May, i.e., MAM) and summer (June-August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August) and MAM 2021. The JJA 2021 seasonal mean was influenced by strong positive anomalies in July due to large and widespread wildfires across the western US. The polluted sites in the western US showed negative O 3 anomalies during MAM 2020 and a slight recovery in 2021, resulting in a positive mean anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The eastern US sites were also characterized by below-mean O 3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the western US sites, which were influenced by wildfires. Concerning the rest of the world, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O 3 detected by the IASI (Infrared Atmospheric Sounding Interferometer) satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O 3 precursor emissions in 2020.

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.

Long et al. (2021) conducted a detailed study of possible interferences in measurements of surface O3 by UV spectroscopy, which measures the UV transmission in ambient and O3-scrubbed air. While we appreciate the careful work done in this analysis, there were several omissions, and in one case, the type of scrubber used was misidentified as manganese dioxide (MnO2) when in fact it was manganese chloride (MnCl2). This misidentification led to the erroneous conclusion that all UV-based O3 instruments employing solid-phase catalytic scrubbers exhibit significant positive artifacts, whereas previous research found this not to be the case when employing MnO2 scrubber types. While the Long et al. (2021) study, and our results, confirm the substantial bias in instruments employing an MnCl2 scrubber, a replication of the earlier work with an MnO2 scrubber type and no humidity correction is needed.