The observed ozone layer depletion is influenced by continuous anthropogenic activity. This fact enforced the regular ozone monitoring globally. Information on spatial-temporal variations in total ozone columns (TOCs) derived by various observational methods and models can differ significantly due to measurement and modelling errors, differences in ozone retrieval algorithms, etc. Therefore, TOC data derived by different means should be validated regularly. In the current study, we compare TOC variations observed by ground-based (Bruker IFS 125 HR, Dobson, and M-124) and satellite (OMI, TROPOMI, and IKFS-2) instruments and simulated by models (ERA5 and EAC4 re-analysis, EMAC and INM RAS—RSHU models) near St. Petersburg (Russia) between 2009 and 2020. We demonstrate that TOC variations near St. Petersburg measured by different methods are in good agreement (with correlation coefficients of 0.95–0.99). Mean differences (MDs) and standard deviations of differences (SDDs) with respect to Dobson measurements constitute 0.0–3.9% and 2.3–3.7%, respectively, which is close to the actual requirements of the quality of TOC measurements. The largest bias is observed for Bruker 125 HR (3.9%) and IKFS-2 (−2.4%) measurements, whereas M-124 filter ozonometer shows no bias. The largest SDDs are observed for satellite measurements (3.3–3.7%), the smallest—for ground-based data (2.3–2.8%). The differences between simulated and Dobson data vary significantly. ERA5 and EAC4 re-analysis data show slight negative bias (0.1–0.2%) with SDDs of 3.7–3.9%. EMAC model overestimates Dobson TOCs by 4.5% with 4.5% SDDs, whereas INM RAS-RSHU model underestimates Dobson by 1.4% with 8.6% SDDs. All datasets demonstrate the pronounced TOC seasonal cycle with the maximum in spring and minimum in autumn. Finally, for 2004–2021 period, we derived a significant positive TOC trend near St. Petersburg (~0.4 ± 0.1 DU per year) from all datasets considered.

Nowadays, different approaches for CO₂ anthropogenic emission estimation are applied to control agreements on greenhouse gas reduction. Some methods are based on the inverse modelling of emissions using various measurements and the results of numerical chemistry transport models (CTMs). Since the accuracy and precision of CTMs largely determine errors in the approaches for emission estimation, it is crucial to validate the performance of such models through observations. In the current study, the near-surface CO₂ mixing ratio simulated by the CTM Weather Research and Forecasting—Chemistry (WRF-Chem) at a high spatial resolution (3 km) using three different sets of CO₂ fluxes (anthropogenic + biogenic fluxes, time-varying and constant anthropogenic emissions) and from Copernicus Atmosphere Monitoring Service (CAMS) datasets have been validated using in situ observations near the Saint Petersburg megacity (Russia) in March and April 2019. It was found that CAMS reanalysis data with a low spatial resolution (1.9° × 3.8°) can match the observations better than CAMS analysis data with a high resolution (0.15° × 0.15°). The CAMS analysis significantly overestimates the observed near-surface CO₂ mixing ratio in Peterhof in March and April 2019 (by more than 10 ppm). The best match for the CAMS reanalysis and observations was observed in March, when the wind was predominantly opposite to the Saint Petersburg urbanized area. In contrast, the CAMS analysis fits the observed trend of the mixing ratio variation in April better than the reanalysis with the wind directions from the Saint Petersburg urban zone. Generally, the WRF-Chem predicts the observed temporal variations in the near-surface CO₂ reasonably well (mean bias ≈ (−0.3) − (−0.9) ppm, RMSD ≈ 8.7 ppm, correlation coefficient ≈ 0.61 ± 0.04). The WRF-Chem data where anthropogenic and biogenic fluxes were used match the observations a bit better than the WRF-Chem data without biogenic fluxes. The diurnal time variation in the anthropogenic emissions influenced the WRF-Chem data insignificantly. However, in general, the data of all three WRF-Chem model runs give almost the same CO₂ temporal variation in Peterhof in March and April 2019. This could be related to the late start of the growing season, which influences biogenic CO₂ fluxes, inaccuracies in the estimation of the biogenic fluxes, and the simplified time variation pattern of the CO₂ anthropogenic emissions.

Atmospheric ozone plays an important role in the biosphere’s absorbing of dangerous solar UV radiation and its contributions to the Earth’s climate. Nowadays, ozone variations are widely monitored by different local and remote sensing methods. Satellite methods can provide data on the global distribution of ozone and its anomalies. In contrast to measurement techniques based on solar radiation measurements, Fourier-transform infrared (FTIR) satellite measurements of thermal radiation provide information, regardless of solar illumination. The global distribution of total ozone columns (TOCs) measured by the IKFS-2 spectrometer aboard the “Meteor M N2” satellite for the period of 2015 to 2020 is presented. The retrieval algorithm uses the artificial neural network (ANN) based on measurements of TOCs by the Aura OMI instrument and the method of principal components for representing IKFS-2 spectral measurements. Latitudinal and seasonal dependencies on the ANN training errors are analyzed and considered as a first approximation of the TOC measurement errors. The TOCs derived by the IKFS-2 instrument are compared to independent ground-based and satellite data. The average differences between the IKFS-2 data and the independent TOC measurements are up to 2% (IKFS-2 usually slightly underestimates the other data), and the standard deviations of differences (SDDs) vary from 2 to 4%. At the same time, both the analysis of the ANN approximation errors of the OMI data and the comparison of the IKFS-2 results with independent data demonstrate an increase in discrepancies towards the poles. In the spring–winter period, SDDs reach 8% in the Southern and 6% in the Northern Hemisphere. The technique presented can be used to process the IKFS-2 spectral data, and as a result, it can provide global information on the TOCs in the period of 2015–2020, regardless of the solar illumination and the presence of clouds.

Ozone is one of the key atmospheric trace gases that protects life on Earth from harmful solar ultraviolet radiation. The importance of ozone and its influence on various atmospheric processes led to the creation of a global ozone monitoring system. Satellite remote sensing methods make a significant contribution to the analysis of spatial distribution and temporal evolution of ozone content and its anomalies. We have presented a global (60° S – 90° N) total ozone column (TOC) data product derived using the IKFS-2 (Infrared Fourier Spectrometer) instrument aboard the Meteor-M N2 satellite in 2019–2020. The IKFS-2 measures outgoing thermal radiation in 5–15 µm (660–2000 cm⁻¹) spectral range with an un-apodized spectral resolution of 0.4 cm⁻¹. The retrieval technique is based on the artificial neural network (ANN) algorithm and the method of principal components. For the ANN training, we used the level 2 TOC measurements by the OMI instrument aboard the Aura satellite, thus solving the problem of calibrating the IKFS-2 TOC data product. The uncertainty estimated for IKFS-2 TOC measurements equals 10 DU (~3%). We compared IKFS-2 TOC data with ground-based measurements at 14 observational sites in the Northern Hemisphere equipped with the Dobson and Brewer spectrometers. For spatial mismatch of 70 km and temporal mismatch of 1 h, we observed the mean bias between IKFS-2 and ground-based (direct solar measurements) TOC datasets of −1.46% with a standard deviation of 2.57%. We demonstrated that global distribution fields of IKFS-2 TOCs are consistent with OMI measurements and ECMWF ERA5 reanalysis data. In general, for seasonal means, IKFS-2 underestimates OMI TOCs by 0.1–1.0%, and ERA5 TOCs by 1.8–3.1%, depending on the season. We analysed IKFS-2 TOC retrievals in 2018/2019 versus 2019/2020 Arctic winters. Differences in TOCs resulting from differences in stratospheric dynamic for two winter-spring seasons are well captured by IKFS-2. In 2020 relative to 2019, the averaged (50–90° N) deficit in TOCs comprises 72 DU (17%), 68 DU (16%), 84 DU (20%), and 70 DU (17%) for January, February, March, and April, respectively. Dataset presented is the first data product of continuous TOC measurements derived using publicly available IKFS-2 level 1b data. Further, we plan to obtain, analyze, and validate IKFS-2 TOCs for an extended period of measurements (2015–2020).

The increase of the CO₂ content in the atmosphere caused by anthropogenic emissions from the territories of large cities (~70%) is the critical factor in determining the accuracy of emission estimations. Advanced experiment-based methods of anthropogenic CO₂ emission estimation are based on the solution of an inverse problem, using accurate measurements of CO₂ content and numerical models of atmospheric transport and chemistry. The accuracy of such models decreases the errors of the emission estimations. The aim of the current study is to adapt numerical weather prediction and atmospheric chemistry model WRF-Chem and validate its capability to simulate atmospheric CO₂ for the territories of the two large coastal cities of the Gulf of Finland—St. Petersburg (Russia) and Helsinki (Finland). The research has demonstrated that the WRF-Chem model is able to simulate annual variation, as well as the mean seasonal and diurnal variations of the near-surface CO₂ mixing ratio, in Helsinki, at a high spatial resolution (2 km). Correlation between the modelled and measured CO₂ mixing ratio is relatively high, at ~0.73, with a mean difference and its standard deviation of 0.15 ± 0.04 and 1.7%, respectively. The differences between the WRF-Chem data and the measurements might be caused by errors in the modelling of atmospheric transport and in a priori CO₂ emissions and biogenic fluxes. The WRF-Chem model simulates well the column-averaged CO₂ mixing ratio (XCO₂) in St. Petersburg (January 2019–March 2020), with a correlation of ~0.95 relative to ground-based spectroscopic measurements by the IR–Fourier spectrometer Bruker EM27/SUN. The error of the XCO₂ modelling constitutes ~0.3%, and most likely is related to inaccuracies in chemical boundary conditions and a priori anthropogenic CO₂ emissions. The XCO₂ time series in St. Petersburg by the WRF-Chem model fits well with global CAMS reanalysis and CarbonTracker-modelled data (the differences are less than ~1%). However, due to much higher spatial resolution (2 vs. over 100 km), the WRF-Chem data are in the best agreement with the ground-based remote measurements of XCO₂. According to the study, the modelling errors of XCO₂ in St. Petersburg during the whole simulated period are sufficiently minimal to fit the requirement of “Error ≤ 0.2%” in 60% of cases. This requirement should be satisfied to evaluate properly the anthropogenic CO₂ emissions of St. Petersburg on a city-scale.

The importance of controlling total ozone (TO) and surface ultraviolet irradiance is a reason for continuous monitoring of the ozonosphere using various gr