Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)

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

Validation of WRF-Chem Model and CAMS Performance in Estimating Near-Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)

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

Measurements and Modelling of Total Ozone Columns near St. Petersburg, Russia

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

Six Years of IKFS-2 Global Ozone Total Column Measurements

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

Measurements and Modelling of Total Ozone Columns near St. Petersburg, Russia

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 (similar to 0.4 +/- 0.1 DU per year) from all datasets considered.ISSN:2072-429