Submitted as: model evaluation paper
06 Sep 2022
Submitted as: model evaluation paper | 06 Sep 2022
Status: this preprint is currently under review for the journal GMD.

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

Aleks Lacima1, Hervé Petetin1, Albert Soret1, Dene Bowdalo1, Oriol Jorba1, Zhaoyue Chen2,3, Raul F. Méndez Turrubiates2, Hicham Achebak2, Joan Ballester2, and Carlos Pérez García-Pando1,4 Aleks Lacima et al.
  • 1Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
  • 2ISGlobal, Barcelona, Spain
  • 3Universitat Pompeu Fabra (UPF), Barcelona, Spain
  • 4ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain

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 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 O3, CO, PM10 and PM2.5 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.

Aleks Lacima et al.

Status: open (until 02 Nov 2022)

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  • RC1: 'Comment on gmd-2022-197', Anonymous Referee #1, 27 Sep 2022 reply

Aleks Lacima et al.

Aleks Lacima et al.


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Short summary
Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Sumpercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modelled surface pollution.