Alexander V. Polyakov

Anomalous ozone loss in the Northern Hemisphere during winter–spring periods has been increasingly observed in recent decades. Regular monitoring of stratospheric gases involved in ozone-depleting processes is essential for predicting the appearance of ozone mini-holes and the growth in UV surface irradiance. The St. Petersburg State University site located on the border of mid- and high latitudes is equipped with the Bruker IFS 125HR spectrometer for trace gas measurements and the UV and VIS Ocean Optics HR4000 spectrometers for total ozone column (TOC) measurements. The harmonization of TOC series from three instruments made it possible to analyze TOC variability during rapid changes in air masses. A decrease in ozone in the 8–18 km (−0.41 ± 0.33% yr⁻¹) and 18–25 km (−0.17 ± 0.15% yr⁻¹) layers, as well as in stratospheric HNO₃ (−0.31 ± 0.18% yr⁻¹), is observed for the 2009–2026 period, along with an increase in HF (+0.41 ± 0.20% yr⁻¹) mainly due to changes in atmospheric dynamics over the past decade. An observed decrease in CCl₃F (−0.43 ± 0.06% yr⁻¹), CCl₂F₂ (−0.71 ± 0.04% yr⁻¹), ClONO₂ (−1.14 ± 0.82% yr⁻¹), and HCl (−0.31 ± 0.16% yr⁻¹) indicates a decrease in the chlorine-containing gases in the atmosphere. A comparison of the EMAC and SOCOLv3 simulations with observations for the common 2009–2019 period showed that ozone variability, including trends, seasonal variations, and rapid winter–spring changes, is described by the models within the measurement uncertainty limits, whereas for HCl, ClONO₂, and HNO₃, the quantitative discrepancy between the simulations and measurements exceeds the corresponding measurement uncertainty.

To improve diagnostics and prediction of changes caused by increased impact of anthropogenic activity, it is necessary to increase the comparative analysis of measurements and modeling of ozone—one of the climatically important atmospheric gases due to the decisive influence of stratospheric ozone on the radiation balance of the Earth-atmosphere system and the role of tropospheric ozone, the third most significant anthropogenic factor contributing to the greenhouse effect. This task is particularly relevant for Russia, as its geographical location makes it more vulnerable to climate change than other countries, whereas its regional tendencies in ozone variability have not yet been studied in sufficient detail. An analysis of IKFS-2 tropospheric ozone content (TrOC) measurements for 2015–2022 revealed that in Siberian, Far Eastern, North Caucasian, and Southern federal districts of Russia TrOC maximum, caused by photochemical formation of ground-level ozone, is observed in July (up to 30–35 DU for monthly means in surface-400 hPa layer). In Northwestern federal district, TrOC maximum (up to 25–30 DU), determined by meridional transport, is observed in late spring. No statistically significant linear trends in TrOC are detected. The WRF-Chem model qualitatively describes the seasonal variations of TrOC as well as the anomalous increase in TrOC caused by forest fires. The variability of total ozone content (TOC) is analyzed by OMI (2005–2023) and IKFS-2 (2015–2022) measurements as well as by SOCOLv3 simulations. Ozone negative anomalies in spring (up to 15% for monthly means) are generally observed with positive Arctic oscillation index values and a westerly phase of Quasi-biennial oscillations. For the 2008–2022 period, a statistically significant increase in TOC (+1.6–1.7% per year) is obtained for European Russia and Western and Central Siberia in November.

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.

Several chemistry-climate models (CCM) tend to underestimate the total column ozone (TCO) over the southern polar region during winter. The most significant discrepancies were found in the CCM SOCOLv3, which we utilize to study the sensitivity of TCO over Antarctica to the following key influencing factors: (1) the efficiency of stratospheric heterogeneous reactions, (2) the intensity of meridional flux into the polar regions due to sub-grid scale mixing processes, and (3) the accuracy of photodissociation rate calculations. Comparisons of the model results with satellite observations from IKFS-2, MLS, and MIPAS showed that the most effective processes for improving polar ozone simulation are photolysis and horizontal mixing. An increase in horizontal mixing improves the simulated TCO seasonal cycle; however, it negatively impacts the representation of the CH4 and N2O distributions. The application of the photolysis module Cloud-J v.8.0 has improved the accuracy of photolysis rate calculations and the representation of the seasonal ozone cycle over the southern polar region. This paper outlines the impact of various processes on the performance of chemistry-climate models in the southern polar stratosphere, with potential implications for future advancements in this area.

Stratospheric ozone shields life on Earth from harmful ultraviolet radiation and plays a crucial role in climate formation, while tropospheric ozone is a pollutant and greenhouse gas. Satellite methods based on measurements of outgoing thermal radiation are the only methods that provide information on global ozone distribution, independent of solar illumination. Since about 90% of atmospheric ozone is concentrated in the stratosphere, ozone total column measurements can be used as stratospheric ozone measurements. We present techniques for deriving information on total ozone columns (TOCs) and tropospheric ozone columns (TrOCs) from spectra of outgoing thermal radiation measured by the IKFS-2 instrument aboard the Meteor-M No. 2 satellite. The techniques are based on principal component analysis and the artificial neural network approach, providing high accuracy in TOC (less than 3%) and TrOC (within 2–4 DU) retrieval in accordance with the WMO requirements for the quality of satellite measurements.

The results of determining the ozone total column (OTC) from the spectra of the outgoing thermal infrared radiation measured by the IKFS-2 instrument from the Meteor-M No. 2 spacecraft during 8 years of measurements are presented. The previously developed technique for the interpretation of spectral measurements made in 2015–2020 with a swath width (SW) of 1000 km is applied to the measurements in 2021–2022 with a SW of 1500 km. It is shown that the increase in the differences between the IKFS-2 data and the results of independent measurements is caused not by the expansion of the OTC variability statistics, but by the increase in the range of scanning angle variation. After a finalization of the technique for the measurements with a 1500-km SW, a comparison with independent data showed that the standard deviations of differences with the results of ground and satellite measurements for all 8 years do not exceed 3% and did not increase compared to the first 6 years of measurements. To analyze the results in the polar regions, the OTC values obtained from the IKFS-2 spectra are compared with the ozonesonde data, which are performed continuously throughout the year, including the polar night. A good qualitative agreement of the IKFS-2 data and ozone sounding data, including winter–spring periods of extreme OTC decrease at high latitudes of both hemispheres, is shown. The standard deviations of the differences between the IKFS-2 data and the OTC values from ozone sounding data were from 5.3 to 11% (17–33 DU) for different stations, or on average for all stations 7.9%, which is consistent with the uncertainty of the estimates of the integrated ozone content in the vertical column from ozonesonde data.

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities.

HCFC‐22 is an ozone‐depleting substance with a greenhouse effect. The atmospheric mole fractions of HCFC‐22 have been increasing since the 1950s. Within the NDACC‐IRWG network, HCFC‐22 mol fractions can be retrieved from solar absorption spectra measured by ground‐based FTIR. However, only a few sites have provided HCFC‐22 data sets. Here, we demonstrate a harmonized FTIR HCFC‐22 retrieval strategy and generate a new global NDACC‐IRWG HCFC‐22 data set at 16 FTIR sites. The systematic and random uncertainties are 5.3%–8.7% and 3.2%–8.0%, respectively. A maximum HCFC‐22 column annual growth rate was observed in 2009 with a mean of 7.65 ± 1.39 ppt/year, and the HCFC‐22 annual growth rate decreased to 3.57 ± 1.39 ppt/year (2016–2020) and 2.15 ± 2.09 ppt/year (2021–2023). The annual growth rates derived from the FTIR measurements are compared to the ones derived from NOAA surface flask samplings and ACE‐FTS satellite measurements, and the three independent data sets show a good agreement.

A technique for determining the tropospheric ozone (TO) content from the spectra of outgoing thermal infrared (IR) radiation based on the principal component method and neural network approach is proposed. To train the artificial neural networks, TO data calculated from ozone profiles of vertical ozone content derived from ozonesondes are used. The TCO is considered the ozone content in the atmospheric layers from the earth’s surface to pressure levels of 400 and 300 hPa. The error of approximating TO values on training data is 2.7 and 3.6 DU for layers below 400 and 300 hPa, respectively. The methodology is validated on the basis of comparison with ground-based TO measurements at the NDACC international observing network of stations using solar infrared spectra. The mean standard deviations of the differences between the ground-based infrared measurements at 19 stations and the derived TO values from the IKFS-2 data were about 3 DU. The mean differences depend on the altitude and geographical location of the ground station, varying from +3 to –12 DU. The discrepancies between the ground-based measurements and satellite data correspond to the results of other authors obtained for the IASI satellite instrument, which is close in characteristics. The paper presents examples of the global distribution of mean monthly TO values for different seasons.