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Article

Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3

by
Anastasia Imanova
1,2,
Tatiana Egorova
3,*,
Vladimir Zubov
1,4,
Andrey Mironov
1,
Alexander Polyakov
1,
Georgiy Nerobelov
1 and
Eugene Rozanov
1,3
1
Laboratory for the Study of the Ozone Layer and the Upper Atmosphere, Saint-Petersburg State University, 199034 Saint Petersburg, Russia
2
Faculty of Meteorology, Russian State Hydrometeorological University, 195196 Saint Petersburg, Russia
3
Physikalisch-Meteorologisches Observatorium and World Radiation Center, CH-7260 Davos, Switzerland
4
Voeikov Main Geophysical Observatory, 194021 Saint Petersburg, Russia
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(10), 1172; https://doi.org/10.3390/atmos16101172
Submission received: 7 August 2025 / Revised: 29 September 2025 / Accepted: 7 October 2025 / Published: 9 October 2025
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Abstract

The severe ozone depletion over the Southern polar region, known as the “ozone hole,” is a stark example of global ozone depletion caused by human-made chemicals. This has implications for climate change and increased harmful surface solar UV. Several Chemistry–Climate models (CCMs) tend to underestimate total column ozone (TCO) against satellite measurements over the Southern polar region. This underestimation can reach up to 50% in monthly mean zonally averaged biases during cold seasons. The most significant discrepancies were found in the CCM SOlar Climate Ozone Links version 3 (SOCOLv3). We use SOCOLv3 to study the sensitivity of Antarctic TCO to three key factors: (1) stratospheric heterogeneous reaction efficiency, (2) meridional flux intensity into polar regions from sub-grid scale mixing, and (3) photodissociation rate calculation accuracy. We compared the model results with satellite data from Infrared Fourier Spectrometer-2 (IKFS-2), Microwave Limb Sounder (MLS), and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The most effective processes for improving polar ozone simulation are photolysis and horizontal mixing. Increasing horizontal mixing improves the simulated TCO seasonal cycle but negatively impacts CH4 and N2O distributions. Using the Cloud-J v.8.0 photolysis module has improved photolysis rate calculations and the seasonal ozone cycle representation over the Southern polar region. This paper outlines how different processes impact chemistry–climate model performance in the southern polar stratosphere, with potential implications for future advancements.

1. Introduction

The atmospheric ozone (O3) layer is an essential factor for the sustainability of the Earth’s biosphere and human health. It shields all living organisms from the Sun’s harmful ultraviolet radiation in the UV-C and UV-B bands of the solar spectrum. Additionally, as a radiatively active gas, ozone contributes substantially to the energy balance of the Earth [1].
Stratospheric ozone has been depleted since the 1970s due to emissions of chlorine and bromine-containing substances. In the 1980s, the ozone hole was discovered in the Southern Hemisphere at a high-latitude polar station [2]. In direct response, governments around the world signed the Montreal Protocol to limit the production of chlorofluorocarbons (CFCs), which came into force in 1989. To assess the effectiveness of the Montreal Protocol and to understand the evolution of atmospheric ozone in the 21st century, a special class of models called Chemistry–Climate models (CCM) has been employed [3]. CCM considers the main processes responsible for the ozone layer state and enables calculations of the fundamental characteristics of the Earth’s atmospheric circulation [4,5,6].
Typically, CCM performance at high latitudes has been evaluated only during periods when solar light is available. Studies have shown that many models perform well over the southern high latitudes in early spring [6,7,8]. The lack of model validation during the polar night is primarily because, until 2014, most satellite instruments were unable to measure the ozone in the polar latitudes of the Southern Hemisphere since there was no solar light available. The only TOVS (The TIROS Operational Vertical Sounder) satellite instrument has performed measurements of TCO during the polar night, but its relative measurement error was too high [9]. The situation changed with the introduction of spectrometers that operate in the infrared range. One such device is the IKFS-2, which provides data on the ozone content under both night and day conditions with an accuracy of 5% [10]. As a result, further global monitoring efforts aimed at quantifying the ozone trend analysis over recent years will help to advance our understanding of potential issues, such as inaccuracies in the treatment of physical, chemical, and dynamical processes in the models.
Figure 1 illustrates the climatological annual cycle of TCO (in Dobson Units or DU) averaged over 75–80° S from satellite measurements with IKFS-2 and SBUV sensors together with the results of SOCOLv3, CMAM, MIROC 3.2, NIWA-UKCA, GEOSCCM, MRI-ESM, and ULAQ chemistry–climate models. The SBUV data were acquired from https://acd-ext.gsfc.nasa.gov/Data_services/merged (accessed on 29 September 2025). The model data were acquired from the CCMI-1 data archive (https://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1, accessed on 29 September 2025). The SOCOLv3 data for 2014–2018 were produced using the CCM SOCOLv3 version used in CCMI-1.
Figure 1 shows a good agreement between SBUV and IKFS2 data during the austral spring and summer seasons (October-February), which is when the SBUV data are available. The comparison of SOCOLv3 data from 1990 to 2009 and 2014 to 2018 shows minor changes in TCO behavior during autumn and winter. All CCMs tend to underestimate polar TCO values from April to July. Among these, the GEOSCCM exhibits the smallest deviation, while SOCOLv3 shows the largest error (~100 DU) compared to the IKFS-2 data. From October to December, SOCOLv3, GEOSCCM, MRI-ESM, and ULAG align with the IKFS-2 data within the uncertainty range. In contrast, MIROC3.2, CMAM, and NIWA-UKCA show some underestimation. Consequently, the TCO levels in October, which are often used to assess model performance in simulating the ozone hole, do not provide comprehensive information about model quality. The polar night area is particularly noteworthy because virtually all models indicate lower TCO compared to satellite data. The reasons behind this TCO underestimation are currently unknown, presenting a significant gap in our understanding of the processes that define the state of the ozone layer. This study shows an improvement in the representation of the southern polar TCO in the CCM SOCOLv3, as there is full access to this model, where the issues are most significant. Several factors affecting the annual polar ozone cycle in the Southern Hemisphere were considered: (1) the efficiency of the heterogeneous reactions; (2) the intensity of the sub-grid-scale meridional mixing (into the polar vortexes), and (3) the accuracy of the photolysis rate calculations for the large solar zenith angles. To evaluate the impact of photolysis rates for large solar zenith angles, a new version of CCM SOCOLv3 was applied that incorporates the Cloud-J v.8.0 module for the photolysis rate calculation [11].
Section 2 presents a brief description of the observation data used for comparison, including CCM SOCOLv3 and Cloud-J v.8.0, along with sensitivity experiments conducted. The results are described and discussed in Section 3 and Section 4, respectively, and the main conclusions are presented in Section 5.

2. Materials and Methods

2.1. Data Used for Comparison

To compare the model data with actual observations, data from the IKFS-2 instrument, which is onboard the Meteor-M N2 series satellite, were used. This instrument is designed to measure the spectra of outgoing radiation from the atmosphere-surface system, essential for obtaining various types of information, including temperature and humidity profiles, total ozone content, surface temperature, fraction of cloud cover, and pressure at the upper cloud boundary. The purpose, design, and main characteristics of IKFS-2 are presented in [10]. The study in [12] analyzes the operation of IKFS-2, including an assessment of measurement quality (errors in radiometric and spectral calibration) and their information content. The TCO processing algorithms are described in [13]. The IKFS-2 data have been available since March 2015, and for our comparison, data from 2016 to 2020 were utilized.
Two different instruments were used to compare the simulated vertical profiles of O3, N2O, and CH4: the MLS on board NASA’s Aura satellite and the MIPAS. MLS measures microwave thermal radiation from the limb of the atmosphere using seven radiometers [14]. This instrument provides vertical profiles of atmospheric components with high spatial coverage, enabling detailed observations of trace gases. MIPAS is a mid-infrared emission spectrometer that is part of the core payload of the ENVISAT satellite. As a limb sounder, it scans across the horizon to detect atmospheric spectral radiances, which are then inverted to derive vertical temperature profiles, trace species concentrations, and cloud distributions. [15]. For this analysis, we use MLS data covering the period from 2014 to 2018 and MIPAS data from 2008 to 2012.
Due to the availability and quality of the observation data, this study uses different data periods for comparison with model data. Table 1 contains all the data sets used in this study and their periods.

2.2. Cloud-J v.8.0

Cloud-J v.8.0 is a broadband algorithm designed for calculating photolysis rates in the presence of cloud and aerosol layers. This algorithm enables the simulation of global atmospheric photochemistry by directly incorporating the physical properties of scattering and absorbing particles. The radiative transfer calculations in Cloud-J v.8.0 are based on the Feautrier method for solving the radiative transport equations in a plane-parallel atmosphere. The Cloud-J v.8.0 spectral range (177.5–778 nm) is divided into 18 spectral intervals (bins). Spectral data (absorption cross sections and dissociation quantum yields) are calculated based on laboratory data, in particular, JPL and IUPAC [16,17].
A complete set of equations, a description of the methods for accounting for processes associated with the passage of solar radiation, and a detailed description of the spectral range can be found in [11,18]. Comparison of data obtained by Cloud-J v.8.0 and libRadtran [19], as well as the sensitivity of the module to the atmospheric parameters, is presented in the paper [20].
The photolysis rates in SOCOLv3 are calculated using the look-up table method (LUT) [21]. In the case of large zenith angles of the Sun, spherical geometry is included in the model calculation of the photolyze rates. This approach provides the not-zero photodissociation values even when the solar zenith angle exceeds 90°, but the accuracy of this addition to the LUT scheme was not qualified.
The new photolysis rate calculation module Cloud-J v.8.0 includes solar flux calculations for solar zenith angles > 90°, considering spherical raytracing, extinction of the direct solar beam, and a true geometric atmosphere instead of a flat geopotential atmosphere used in most models [22], which facilitates more accurate calculation of photolysis rates.

2.3. CCM SOCOLv3

The CCM SOCOLv3 calculates a wide range of atmospheric dynamic processes interactively. These include the hydrological cycle, cloud formation, convection, solar and thermal radiation fluxes, photolysis, chemical transformations, and vertical and horizontal transport of species. The main components of the model are illustrated in Figure 2.
The CCM SOCOLv3 consists of the general circulation model (GCM) of the middle atmosphere ECHAM5 and the chemical module (CM) MEZON. GCM and CM are interactively linked through 3D fields of temperature, winds (from GCM to CM), as well as radiatively active species such as water vapor, ozone, methane, nitrous oxide, and chlorofluorocarbons (from CM to GCM). The model includes 41 chemical elements from the groups of oxygen, hydrogen, nitrogen, carbon, chlorine, and bromine. Interactions between gas species are determined by 140 gas phase reactions, 46 photolysis reactions, and 16 heterogeneous reactions on liquid sulfate aerosols and solid particles of water ice and nitric acid trihydrate [23]. As external drivers of the atmospheric state, the model uses greenhouse gases and ozone-depleting substances mixing ratios, sea surface temperature (SST) and sea ice concentration (SIC), spectral solar radiation, sulfate aerosols properties, and some other parameters. To calculate photolysis rates in SOCOLv3, the default LUT method is used. To calculate photolysis rates in SOCOLv3, the default Look-Up Table (LUT) method is employed. In one of the model sensitivity experiments (SR3), the Cloud-J version 8.0 module was installed to calculate photolysis rates. This module receives solar irradiance and atmospheric parameters from ECHAM5 and MEZON and subsequently feeds the calculated photolysis rates back to MEZON. The SOCOLv3 is structured with 39 vertical levels, extending from the surface up to 0.01 hPa, and operates a horizontal resolution of T42. The model can be run very efficiently in parallel mode.
The model’s performance was evaluated successfully within the framework of the international model intercomparison project CCMI-1 [24]. A detailed description of SOCOLv3 can be found in [23].

2.4. Description of the Model Experiments

2.4.1. Reference Experiment (RR)

To compare the original calculation results of the CCM SOCOLv3 with the IKFS-2 measurements, a 19-year-long reference numerical experiment was conducted for the 2000–2018 period. The first 14 years (2000–2013) of the model calculations were considered as a spin-up period, which is essential for the adaptation of the model’s atmospheric chemical composition and dynamics to the specific boundary conditions. The last 5 years of the model experiment (2014–2018) were used for analysis and comparison with the corresponding IKFS-2 observations.
The long-term (2000–2018) evolution of ozone-depleting substances (ODS), greenhouse gas concentrations (GHGs), sea surface temperature and sea ice concentration (SST/SIC), as well as zonal winds in the equatorial stratosphere (QBO) are considered as boundary conditions. The mixing ratios of ODS in the lower troposphere were based on the data from the World Meteorological Organization (WMO) [25]. The atmospheric mixing ratios for the primary GHG (CO2, CH4, and N2O) are taken from [26] until 2014 and extended to 2018 following the IPCC SSP2-4.5 scenario [27].
The SST/SIC fields for the 21st century, prescribed as monthly means, are adopted from the HadISST1 data set provided by the UK Met Office Hadley Centre [28]. The QBO is produced by a linear relaxation (“nudging”) of the model zonal winds in the equatorial stratosphere to a time series of observed winds [29]. The nudging is used between 20° N and 20° S from 90 hPa up to 3 hPa. Within the QBO core domain (10° N–10° S, 50–8 hPa), the relaxation time is uniformly set to 7 days; outside this region, the damping depends on latitude and altitude [30]. Also, the evolutions of the 11-year solar activity [31], stratospheric aerosol contents [32], and the surface CO and NOx emissions from CMIP6 input4MIPs databases for the historical period to 2014 and following RCP2-4.5 until 2018 [33] were applied in the model experiments as boundary conditions.

2.4.2. Sensitivity Experiments (SR1, SR2, SR3)

The TCO discrepancies between model and satellite data (Figure 1) can likely be attributed to inadequate model representations of the processes influencing the polar ozone formation. According to the modern scientific paradigm, the ozone content in the polar stratosphere is controlled mainly by the rates of heterogeneous reactions and the photodissociation of ozone molecules by solar radiation at the large zenith angles of the Sun [34]. Also, the level of TCO inside the inner part of the SH polar night vortex can depend on the horizontal resolution of the model [35]. So, if the model grid is coarse, it becomes essential to account for the transport of model species into the polar night vortex through the sub-grid scale motions.
Therefore, to investigate the reasons behind the simulated TCO underestimation and improve the accuracy of the model, a set of additional numerical experiments using CCM SOCOLv3 were conducted, as described in Table 2. All these model experiments were set up exactly as the reference case, maintaining the same boundary conditions, spin-up processes, and using model results from the years 2014 to 2018 for comparison with satellite data.
Figure 3 illustrates the annual variability of TCO simulated with the SOCOLv3, averaged over the periods that are used for comparison with observations. The results indicate that the use of different data periods is acceptable and does not significantly impact the result. To assess statistical significance, Student’s t-test has been applied, ensuring that interpretations are robust and supported by reliable statistical analysis.

3. Results

Figure 4 illustrates the TCO calculated in the reference experiment of SOCOLv3 and the corresponding values of TCO obtained from IKFS-2 observations. The figure indicates that the model significantly underestimated the TCO values in the polar region of the SH compared to the satellite data. In August the difference exceeds 90 DU.

3.1. Sensitivity to Heterogeneous Chemistry (SR1)

Chemical reactions on/in polar stratospheric cloud particles play a crucial role in the formation of the Antarctic ozone hole [36]. To analyze the model TCO sensitivity to the intensity of heterogeneous processes, the rate of the most important heterogeneous reaction HCl + ClONO2 → Cl2 + HNO3 was reduced by half. This reaction is the primary source of the Cl2, which in turn produces active chlorines that destroy polar stratospheric ozone in a photo-catalytic cycle following the polar sunrise.
Figure 5 shows the annual variability of monthly mean TCO at 80° S averaged from 2016 to 2018, based on experiments together with the corresponding values from the reference experiment and the IKFS-2 measurements.
Table 3 presents the mean bias and determination coefficient (R2) for the model experiments in comparison to the IKFS-2 satellite data. The reference experiment shows a bias of 108 DU in August and 74 DU in September. For the SR1 experiment, the biases are recorded as 96 DU in August and 53 DU in September, with an R2 value of 0.22.
As expected, reduced efficiency of the HCl + ClONO2 → Cl2 + HNO3 reaction rate leads to smaller ozone depletion and higher TCO only from October to December. However, this rate correction does not significantly impact TCO values within the polar vortex during the polar night and does not play a substantial role during winter.

3.2. Sensitivity to the Subgrid-Scale Mixing (SR2)

Previous numerical experiments using chemistry-transport models provided some evidence that the air mass exchange between the polar vortex area and the middle latitude in the SH stratosphere depends on the applied horizontal resolution [35,37]. The calculated mass fluxes into the inner region of the vortex from outside increase when the model’s horizontal resolution is higher. It can be explained by the fact that the model with the higher horizontal resolution generates additional transport by the atmospheric motions that cannot be resolved by the model with the coarse grid.
To verify this suggestion for SOCOLv3, we included in the model’s lower stratosphere a zonally averaged meridional diffusion of the species with a coefficient Kyy which depends only on time and latitude. According to the Prandl mixing length theory, the maximum values of Kyy can be estimated as < 6 × 106 m2/s (mixing length < 3 × 105 m and meridional wind variations <20 m/s on the model meridional grid scale (~2.50)). It was also suggested that the Kyy reached its maximum in the areas of the polar vortex locations. The time-latitudinal mask for the Kyy values is presented in Figure 6.
To evaluate the sensitivity of the model calculations to the intensity of the suggested diffusion process, a set of model experiments was undertaken with Kyy varying from 3 × 105 m2/s to 6 × 106 m2/s in its maximum. The most visible result was obtained in the experiment with the maximum value of Kyy = 5 × 106 m2/s (SR2 experiment).
The monthly mean TCO for ~80° S from SR2 together with the corresponding TCO data from the other experiments (RR, SR1, SR3) and IKFS-2 observations are presented in Figure 5 which demonstrates that enhancement of the subgrid-scale mixing process in the model leads to a remarkable decrease in the TCO bias of CCM SOCOLv3 in comparison with the results of the satellite measurements. As presented in Table 3, the bias for the SR2 experiment measures 33 DU in August and 3 DU in September, with an R2 value of 0.64. These results suggest that this simplified approach holds significant potential. Consequently, it could serve as a beneficial parameterization for CCMs with a coarse horizontal resolution (e.g., greater than 10°).
However, the enhancement of the horizontal mixing into southern high latitudes for the SR2 experiment also affects the vertical distribution of other atmospheric species in these regions. To estimate this effect, the vertical profiles of the mixing ratios of simulated CH4, N2O, and O3 over the South Polar region in August were compared with the MIPAS and MLS measurements. Figure 7 shows the results of this comparison for the RR (upper panels: a, b, and c) and SR2 (lower panels: d, e, and f) experiments. The differences in the model ozone mixing ratio in comparison with the MLS data are shown in panels a and d, the differences in the model CH4 mixing ratio relative to the MIPAS data in panels b and e, and the differences in the model N2O concentrations relative to the MLS data in panels c and f, respectively. The analysis of the results shows that in the lower stratosphere over Antarctica, the deviation of the O3 concentrations calculated in the SR2 experiment from the MLS data (d) changes sign and significantly exceeds the absolute value of the corresponding deviations in the RR experiment (a) leading to an improvement in TCO values. In the lower polar stratosphere, the errors of the model methane and nitrous oxide concentrations relative to the measurement data from the MIPAS and MLS instruments in the SR2 experiment (e and f) are significantly larger than the corresponding errors in the RR numerical experiment (b and c). This discrepancy arises because Prandtl’s theory provides an approximate estimate of possible values for Kyy. As a result, the value of Kyy selected is optimal for approximating the TCO values from the SR2 experiment to the IKFS-2 data and introduces significant distortions into the corresponding model vertical profiles of the CH4, N2O, and O3 concentrations when compared to the RR experiment. Thus, it can be assumed that the failure to consider the subgrid-scale mixing processes in our RR experiment is not the main source of the computed TCO errors in the southern polar region for the SOCOLv3 model.

3.3. Improved Photodissociation Rate Calculations Module (SR3)

Another possible cause of the problem may be related to the accuracy of the photodissociation rate calculations in the RR experiment, which were implemented using a simple look-up table (LUT) approach. To improve the photolysis rate estimation in the model, the Cloud-J v.8.0 module was incorporated into the CCM SOCOLv3 code (SR3 experiment). However, since the spectral range of Cloud-J v.8.0 is limited to 177.5–778 nm and does not allow considering gas components in which photodissociation occurs at shorter wavelengths, such as O2(O+O(1D), H2O, and CH4, the photolysis rates of these gases are obtained using the “old” LUT scheme.
According to Table 3, the SR3 experiment shows a bias of 36 DU in August and 34 DU in September, with an R2 value of 0.53. Although these results are less impressive than those from the SR2 experiment, they still indicate a notable improvement over the reference model.
Figure 8 depicts the absolute difference in TCO between the reference simulation and the experiment utilizing the Cloud-J v.8.0 photolysis scheme, compared against IKFS-2 satellite data. The results demonstrate that the implementation of Cloud-J v.8.0 significantly improves TCO modeling over the Southern polar region from June to September. Specifically, the discrepancy with the satellite data in July was reduced from 80 DU in the reference experiment to 40 DU. A similar improvement was observed in August, where the bias was halved from 80 DU to 40 DU. By September, the model experiment with Cloud-J v.8.0 photolysis showed no statistically significant deviation from the IKFS-2 measurements.
To analyze the changes in the simulated atmosphere, we compare photolysis rates and atmospheric chemistry response to the changes in the photolysis rate calculation method.
Figure 9 shows the photolysis rates of O2 and O3 (total) on the edge of the vortex during the polar night for a zenith angle of 93°, calculated using LUT and Cloud-J + LUT. It is shown that the rates of oxygen photolysis calculated using Cloud-J + LUT are higher than those calculated with only LUT, while the rates of ozone photolysis, on the contrary, are lower.
The differences in the photolysis rates can affect ozone content in three different ways. Higher molecular oxygen photolysis (O2 + hv => O + O) can lead to slightly enhanced ozone production via O + O2 + M => O3 + M. Lower ozone photolysis can directly enhance O3 concentration. Weaker production of excited atomic oxygen via O3 + hv => O2 + O(1D) or O(3P) can lead to the redistribution of the active radicals from chlorine (ClOx), hydrogen (HOx), and nitrogen (NOx) groups. The model does not show substantial changes in HOx and NOx; however, for the ClOx family, the changes are substantial. They are demonstrated in Figure 10, which shows a significant decrease in OH and O(1D) and transfer of active chlorines (Cl + ClO + HOCl + CL2 + Cl2O2) to a more passive HCl form. This process is related to slower HCl destruction by hydroxyl and atomic oxygen via HCl + OH and HCl + O.
Differences in the mixing ratios of O3 between the reference experiment (a), SR2 experiment (b), and MLS data for the Southern Hemisphere (45–80° S) averaged over the 2014–2018 period are presented in Figure 11.
The application of the new photolysis module improves the agreement between model results and MLS data in the entire stratosphere during the winter. The underestimation of the ozone mixing ratio remains above 10 hPa, but its magnitude is substantially smaller during June and July and almost insignificant in August. In the lower stratosphere (below 10 hPa), the improvement is the most pronounced during July and August. During these months, the previously simulated substantial negative bias vanishes over high latitudes, with remaining positive biases considerably diminished north of 60° S.
Figure 12 illustrates differences in the mixing ratios of CH4 and N2O between the reference experiment (a), SR2 experiment (b), and MLS and MIPAS data. It indicates that unlike the results from the SR2 experiment, the deviations in the vertical profiles of methane and nitrous oxide from observed values remain consistent. The significant positive biases in the simulated data remain virtually the same, likely due to an underestimation of the downward transport within the polar vortex.

4. Discussion

The availability of high-quality satellite measurements by infrared-based satellite instruments (e.g., IKFS-2) has significantly enhanced the evaluation of the CCM’s performance over polar regions, especially under polar night conditions. Comparison of the TCO simulation results by the CCM SOCOv3 and other CCMs with the satellite data revealed the existence of too-low values of the model TCO during the polar night inside the southern vortex. The causes of the identified problem were not indicated in the available publications. To address this, the CCM SOCOLv3 was used to quantify the sensitivity of the total column ozone over Antarctica to (1) the efficiency of stratospheric heterogeneous reactions, (2) the intensity of the meridional flux into the polar regions due to sub-grid scale mixing processes, and (3) the accuracy of photodissociation rate calculations. Our comparisons of modeling results with corresponding satellite measurements indicate that the efficiency of heterogeneous reactions plays only a minor role during the polar winter. The most important process for improving the simulated polar ozone levels is the accuracy of the photolysis rate calculations at the large zenith angles and the intensity of horizontal mixing into the polar vortices. Increasing the intensity of horizontal mixing significantly improves the simulated TCO seasonal cycle. However, it also negatively impacts the representation of the CH4 and N2O distributions, enhancing the positive bias compared to the MLS and MIPAS observations in the original model version.
The application of the photolysis module Cloud-J v.8.0 allowed for an increase in the accuracy of photolysis rate calculations, thereby improving the representation of the seasonal ozone cycle in the southern polar region. As a result, the TCO difference compared to the IKFS-2 measurements has reduced from over 100 to approximately 40 DU in the Southern Hemisphere.
The model performance at the middle latitudes remains virtually the same, suggesting that the improvements are specifically due to more accurate photolysis rate calculations for high zenith angles. The positive bias in CH4 and N2O concentrations is not sensitive to these photolysis rate calculations. This fact, together with the remaining negative bias in the lower stratospheric ozone, inspires further studies to elucidate possible problems with some other processes. One possible explanation could be related to the underestimation of the intensity of simulated downward transport, which moves air masses with a lower mixing CH4 and N2O ratio and higher ozone content from the middle and upper stratosphere.
In summary, this paper discusses the impact of different processes on the effectiveness of chemistry–climate models in simulating the total ozone in the southern polar atmosphere. By examining factors such as stratospheric heterogeneous reactions, meridional flux intensity, and the accuracy of photodissociation rate calculations, this study identifies critical mechanisms that affect ozone distribution. The insights gained from these findings possess significant implications for the refinement of CCMs, potentially leading to a deeper understanding of ozone layer evolution in response to climate change scenarios and policy interventions.

5. Conclusions

The chemistry–climate model SOCOLv3 was applied to investigate the sensitivity of Antarctic total ozone content to three key factors: the efficiency of stratospheric heterogeneous reactions, the intensity of meridional transport into the polar regions driven by sub-grid scale mixing, and the accuracy of photodissociation rate calculations. The influence of each factor was assessed by comparing model experiments with satellite observations.
It was demonstrated that photolysis rates and horizontal mixing are the most critical processes for accurate polar ozone simulation during the cold season. For instance, while the reference simulation exhibits a large bias of 108 DU in August compared to IKFS-2 data, the experiment with enhanced horizontal mixing (SR2) reduces this bias to 33 DU. This confirms that increased mixing improves the representation of the TCO seasonal cycle. However, this enhancement comes at a cost as it negatively impacts the simulated distributions of CH4 and N2O.
The implementation of the Cloud-J v.8.0 photolysis module significantly improved the accuracy of photolysis calculations under low sun conditions, leading to a better simulation of the ozone seasonal cycle over the Southern polar region. This is evidenced by the reduced bias in the respective experiment (SR3), which is 36 DU.
Despite these improvements, some discrepancies in the estimation of TCO in the southern hemisphere between models and observations persist. Therefore, future research should focus on improving the representation of downward transport from the middle and upper stratosphere. This process is crucial as it brings air masses with lower concentrations of CH4; and N2O and higher ozone abundance, which is key for achieving more accurate simulations of the Antarctic ozone hole.

Author Contributions

Conceptualization, E.R., V.Z., A.I., and T.E.; methodology V.Z. and E.R.; software, A.I., V.Z., G.N., A.P., and A.M.; formal analysis, A.I., V.Z., A.M., E.R., and T.E.; resources, E.R.; data curation, A.I., V.Z., G.N., and A.P.; writing—original draft preparation, A.M.; writing—review and editing, A.I., V.Z., A.M., E.R., and T.E.; visualization, A.I., A.M., V.Z., and E.R.; supervision, E.R.; project administration, E.R.; funding acquisition, E.R. All authors have read and agreed to the published version of the manuscript.

Funding

The work of A.I., E.R., V.Z., A.P., A.M., and G.N. on the model experiments, preparation of the observation data, analysis of the results, and writing of the manuscript was supported by St. Petersburg State University under research grant 124032000025-1. The work of A.I. on numerical tests of the photolysis algorithm was carried out within the framework of the State Task of the Ministry of Science and Higher Education of Russia for the Russian State Hydrometeorological University (project FSZU-2023-0002). The work of T.E. was supported by the Swiss National Science Foundation project 200021L-22814 10001350 (STOA).

Data Availability Statement

The datasets presented and discussed in this study can be found in online repositories https://doi.org/10.5281/zenodo.16062571 (accessed on 29 September 2025).

Acknowledgments

Model simulations have been performed on the SPBU cluster. We acknowledge the help of A. Divin and A. Zarochentsev with the model maintenance and execution. Some numerical tests of Cloud-J v.8.0 were performed on the RSHU cluster with the support of the RSF project 23-77-30008.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Annual cycle of TCO (DU) at 75–80° S according to the satellite measurements with IKFS-2 and SBUV sensors, as well as results from various chemistry–climate models, including SOCOLv3, CMAM, MIROC 3.2, NIWA-UKCA, GEOSCCM, MRI-ESM, and ULAQ. The data averaging periods are as follows: 2014–2018 for SBUV and SOCOLv3, 2016–2020 for IKFS-2 data, and 1990–2009 for all other model data.
Figure 1. Annual cycle of TCO (DU) at 75–80° S according to the satellite measurements with IKFS-2 and SBUV sensors, as well as results from various chemistry–climate models, including SOCOLv3, CMAM, MIROC 3.2, NIWA-UKCA, GEOSCCM, MRI-ESM, and ULAQ. The data averaging periods are as follows: 2014–2018 for SBUV and SOCOLv3, 2016–2020 for IKFS-2 data, and 1990–2009 for all other model data.
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Figure 2. The main components of the CCM SOCOLv3.
Figure 2. The main components of the CCM SOCOLv3.
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Figure 3. Annual cycle of TCO (DU) at 75–80° S simulated with the CCM SOCOLv3 for various averaging periods: 2014–2018, 2016–2020, 2008–2012.
Figure 3. Annual cycle of TCO (DU) at 75–80° S simulated with the CCM SOCOLv3 for various averaging periods: 2014–2018, 2016–2020, 2008–2012.
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Figure 4. TCO (DU) averaged over all Augusts during the 2016–2020 period from IKFS-2 data (a) and during the 2014–2018 period from the reference experiment (b).
Figure 4. TCO (DU) averaged over all Augusts during the 2016–2020 period from IKFS-2 data (a) and during the 2014–2018 period from the reference experiment (b).
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Figure 5. Annual cycle of TCO (DU) at the 80° S obtained from the IKFS-2 measurements (red) averaged over 2016–2020, and the reference RR (black), SR1 (orange), SR2 (blue), SR3 (cyan) experiments, averaged over 2014–2018.
Figure 5. Annual cycle of TCO (DU) at the 80° S obtained from the IKFS-2 measurements (red) averaged over 2016–2020, and the reference RR (black), SR1 (orange), SR2 (blue), SR3 (cyan) experiments, averaged over 2014–2018.
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Figure 6. The time-latitudinal mask of Kyy value (106 m2/s) (SR2 experiment).
Figure 6. The time-latitudinal mask of Kyy value (106 m2/s) (SR2 experiment).
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Figure 7. Difference in O3 (ppmv) between reference simulation and MLS (a), N2O (ppmv) reference simulation and MLS (b), CH4 (ppmv) reference simulation and MIPAS data (c), O3 (ppmv) SR2 experiment and MLS (d), N2O (ppmv) SR2 experiment and MLS (e), CH4 (ppmv) SR2 experiment and MIPAS (f) in August for the Southern Hemisphere (45–80° S), averaged over 2014–2018. Statistically significant results at 95% level are highlighted in color.
Figure 7. Difference in O3 (ppmv) between reference simulation and MLS (a), N2O (ppmv) reference simulation and MLS (b), CH4 (ppmv) reference simulation and MIPAS data (c), O3 (ppmv) SR2 experiment and MLS (d), N2O (ppmv) SR2 experiment and MLS (e), CH4 (ppmv) SR2 experiment and MIPAS (f) in August for the Southern Hemisphere (45–80° S), averaged over 2014–2018. Statistically significant results at 95% level are highlighted in color.
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Figure 8. Difference in TCO (DU) between reference experiment (a), SR3 experiment (b), and IKFS-2 data for months from June to September over the 2014–2018 period. Statistically significant results at the 95% level are highlighted in color.
Figure 8. Difference in TCO (DU) between reference experiment (a), SR3 experiment (b), and IKFS-2 data for months from June to September over the 2014–2018 period. Statistically significant results at the 95% level are highlighted in color.
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Figure 9. O2 and O3 (total) photolysis rates inside the polar vortex at latitude 71° S on 1 August.
Figure 9. O2 and O3 (total) photolysis rates inside the polar vortex at latitude 71° S on 1 August.
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Figure 10. The annual course of the absolute deviation (ppbv) between the reference experiment and the SR3 experiment for HCl and ClOx family (a) and the relative deviation (%) for O3, OH, O(1D) (b) at 70° S for the period 2016–2020.
Figure 10. The annual course of the absolute deviation (ppbv) between the reference experiment and the SR3 experiment for HCl and ClOx family (a) and the relative deviation (%) for O3, OH, O(1D) (b) at 70° S for the period 2016–2020.
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Figure 11. Difference in zonal mean O3 (ppmv) between reference experiment (a), SR2 experiment (b), and MLS observations for the Southern Hemisphere (45–80° S) averaged over the 2014–2018 period. Statistically significant results at 95% level are highlighted in color.
Figure 11. Difference in zonal mean O3 (ppmv) between reference experiment (a), SR2 experiment (b), and MLS observations for the Southern Hemisphere (45–80° S) averaged over the 2014–2018 period. Statistically significant results at 95% level are highlighted in color.
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Figure 12. Difference in N2O (ppbv) between reference experiment and MLS data (a), CH4 (ppmv) reference experiment and MIPAS data (b), N2O (ppbv) SR3 experiment and MLS data (c), CH4 (ppmv) SR3 experiment and MIPAS (d) in August for the Southern Hemisphere (45–80° S), averaged over 2016–2020. Statistically significant results at the 95% level are highlighted in color.
Figure 12. Difference in N2O (ppbv) between reference experiment and MLS data (a), CH4 (ppmv) reference experiment and MIPAS data (b), N2O (ppbv) SR3 experiment and MLS data (c), CH4 (ppmv) SR3 experiment and MIPAS (d) in August for the Southern Hemisphere (45–80° S), averaged over 2016–2020. Statistically significant results at the 95% level are highlighted in color.
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Table 1. Description of data sets used in the study.
Table 1. Description of data sets used in the study.
InstrumentsSatelliteVariablePeriod Used
IKFS-2METEOR N2TCO2016–2020
MLSAURAO3, N2O2014–2018
MIPASENVISATCH42008–2012
Table 2. Description of the sensitivity experiments with CCM SOCOLv3.
Table 2. Description of the sensitivity experiments with CCM SOCOLv3.
Name of Model ExperimentShort Description of the Model Experiment
RRReference experiment described in Section 2.4.1
SR1Two times reduction in the heterogeneous reaction
HCl + ClONO2 → Cl2 + HNO3 rate.
SR2Intensification of the horizontal mixing process of all transported model species into the SH polar vortex with diffusion coefficient Kyy = 5 × 106 m2/s in its maximum.
SR3Using the Cloud-J v.8.0 module installed in the SOCOLv3 model to calculate photolysis rates
Table 3. Statistical indicators of simulated and observed data comparison.
Table 3. Statistical indicators of simulated and observed data comparison.
Statistical IndicatorsMean Bias, DUR2
ExperimentJulyAugustSeptember
RR88108740.09
SR17796530.22
SR2363330.64
SR35036340.53
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Imanova, A.; Egorova, T.; Zubov, V.; Mironov, A.; Polyakov, A.; Nerobelov, G.; Rozanov, E. Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3. Atmosphere 2025, 16, 1172. https://doi.org/10.3390/atmos16101172

AMA Style

Imanova A, Egorova T, Zubov V, Mironov A, Polyakov A, Nerobelov G, Rozanov E. Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3. Atmosphere. 2025; 16(10):1172. https://doi.org/10.3390/atmos16101172

Chicago/Turabian Style

Imanova, Anastasia, Tatiana Egorova, Vladimir Zubov, Andrey Mironov, Alexander Polyakov, Georgiy Nerobelov, and Eugene Rozanov. 2025. "Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3" Atmosphere 16, no. 10: 1172. https://doi.org/10.3390/atmos16101172

APA Style

Imanova, A., Egorova, T., Zubov, V., Mironov, A., Polyakov, A., Nerobelov, G., & Rozanov, E. (2025). Seasonal Cycle of the Total Ozone Content over Southern High Latitudes in the CCM SOCOLv3. Atmosphere, 16(10), 1172. https://doi.org/10.3390/atmos16101172

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