Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003-2020)

Over the last century, our societies have experienced a sharp increase in urban population and fossil-fuelled transportation, turning air pollution into a critical issue. It is therefore key to accurately characterize the spatiotemporal variability of surface air pollution in order to understand its effects upon the environment, knowledge that can then be used to design effective pollution reduction policies. Global atmospheric composition reanalyses offer great capabilities towards this characterization through assimilation of satellite measurements. However, they generally do not integrate surface measurements and thus remain affected by significant biases at ground level. In this study, we thoroughly evaluate two global atmospheric composition reanalyses, the Copernicus Atmosphere Monitoring Service (CAMSRA) and the Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA-2), between 2003 and 2020, against independent surface measurements of O-3, NO2, CO, SO2 and particulate matter (PM; both PM10 and PM2.5) over the European continent. Overall, both reanalyses present significant and persistent biases for almost all examined pollutants. CAMSRA clearly outperforms MERRA-2 in capturing the spatiotemporal variability of most pollutants, as shown by generally lower biases (all pollutants except for PM2.5), lower errors (all pollutants) and higher correlations (all pollutants except SO2). CAMSRA also outperforms MERRA-2 in capturing the annual trends found in all pollutants (except for SO2). Overall, CAMSRA tends to perform best for O-3 and CO, followed by NO2 and PM10, while poorer results are typically found for SO2 and PM2.5. Higher correlations are generally found in autumn and/or winter for reactive gases. Compared to MERRA-2, CAMSRA assimilates a wider range of satellite products which, while enhancing the performance of the reanalysis in the troposphere (as shown by other studies), has a limited impact on the surface. The biases found in both reanalyses are likely explained by a combination of factors, including errors in emission inventories and/or sinks, a lack of surface data assimilation, and their relatively coarse resolution. Our results highlight the current limitations of reanalyses to represent surface pollution, which limits their applicability for health and environmental impact studies. When applied to reanalysis data, bias-correction methodologies based on surface observations should help to constrain the spatiotemporal variability of surface pollution and its associated impacts.