Modelling and validation of near-surface CO2 mixing ratio in Saint-Petersburg urban area, Russia

Since megacities largely determine the amount of anthropogenic CO2 emitted into the atmosphere (up to 70%), which is the most important anthropogenic greenhouse gas, it is crucial to estimate emissions of this gas from a particular city and even on a city-scale. An inverse modelling of CO2 emissions is a promising approach of the emission estimation from the territories of megacities. This approach combines accurate measurements and high-resolution atmospheric chemistry transport modelling. There are studies which already demonstrated that the accuracy of such estimation highly depended on the accuracy of modelling. In our study we carried out the validation of the performance of the Weather Research and Forecasting – Chemistry (WRF-Chem) model and Copernicus Atmosphere Monitoring Service (CAMS) data to simulate near-surface CO2 mixing ratio in the Saint-Petersburg metropolitan area (Russia) for March and April 2019.

To validate the modelled data, we used in-situ observations of near-surface CO2 mixing ratio in the Peterhof suburban area of Saint-Petersburg. The WRF-Chem modelling was carried out in a nesting mode with the finest grid having 3 km horizontal resolution. Three different datasets of CO2 emissions and fluxes were used in modelling: anthropogenic and biogenic fluxes, only time‐varying and only constant anthropogenic emissions. Two CAMS datasets (containing CO2 mixing ratios) were assessed in this study – global analysis and reanalysis.

For this particular studied period, it was found that the reanalysis data with low spatial resolution (1.9×3.8°) corresponds better to local observations than the analysis data with higher resolution (0.15×0.15°) in March and worse in April. Such behavior can be caused by grid spacing, interpolation method, assimilation approach for observations and specific synoptic scale meteorological conditions.

It was although found, the WRF-Chem model was capable to simulate (with the analysis data) near-surface CO2 mixing ratio in the Peterhof suburban area in spring (Fig.1 and 2) on a high spatial resolution (0.03° or about 3 km). An estimated averaged bias and root mean square deviation varied (-0.3) ÷ (-0.9) and 8.6 ÷ 8.8 ppm, respectively. The correlation coefficient was higher than +0.6 for the studied period. The WRF-Chem output based on three selected datasets differed insignificantly. However, the use of the biogenic emissions made the modelled results fit a little bit better to the observations compared with only anthropogenic emissions.

Fig 1a.
Fig 1b.

Fig.1 Hourly variation in near-surface CO2 mixing ratio according to the WRF‐Chem modelling with time‐varying biogenic and anthropogenic fluxes and only with time‐varying anthropogenic emissions and in situ observations in Peterhof for March (a) and April (b) 2019.

Fig 2a.
Fig 2b.

Fig.2 Monthly‐averaged diurnal variation of near‐surface CO2 mixing ratio according to the WRF‐Chem runs and in situ observations in Peterhof for March (a) and April (b) 2019.

It was found that wind characteristics (speed and direction) and a priori CO2 anthropogenic sources in the Peterhof suburban area of Saint-Petersburg are the main factors. These determined the variation of near-surface CO2 mixing ratio in during March and April 2019. Even though the WRF-Chem model simulated the ground-level CO2 mixing ratio relatively well, further modelling and validation of total column content are both needed. It is important for better understanding whether this model is suitable for the inverse modelling of CO2 emissions from the urban area.

More details (including description of methodology, emission and observations datasets, modelling results, and others) are presented in Nerobelov et al (2021) “Validation of WRF‐Chem Model and CAMS Performance in Estimating Near‐Surface Atmospheric CO2 Mixing Ratio in the Area of Saint Petersburg (Russia)”, Atmosphere, 2021, 12, 387. https://doi.org/10.3390/atmos12030387.

Text by Georgii Nerobelov, Saint-Petersburg State University, St. Petersburg, Russia

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