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

Abstract. Over the last century, our societies have experienced a sharp increase in urban population and fossil-fueled transportation, turning air pollution into one of the most critical issues of our time. It is therefore fundamental to accurately characterize the spatiotemporal variability of surface air pollution, in order to understand its effects upon human health and 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 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, CAMSRA and MERRA-2, between 2003 and 2020, against independent surface measurements of O₃, NO₂, CO, SO₂, PM₁₀ and PM_2.5 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 O₃, CO, PM₁₀ and PM_2.5 surface concentrations. Despite its higher spatial resolution and focus on aerosol representation, MERRA-2 only performs better than CAMSRA for SO₂. CAMSRA also outperforms MERRA-2 in capturing the annual trends found in all pollutants. Both reanalyses show a better performance in summer (JJA), in terms of biases and errors, than in winter (DJF), when pollutant concentrations peak, with the exception of O₃. Higher correlations are not necessarily found in JJA, particularly for reactive gases, which show greater correlation values in autumn (SON) and winter. 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 constraining the spatiotemporal variability of surface pollution and its associated impacts.