Long-term chemical composition of aerosols over the North Indian Ocean: Comparison of cruise-based measurements and global reanalysis datasets

The natural and anthropogenic emissions in South and Southeast Asia have a significant impact on the Indian Ocean, particularly over the North Indian Ocean (NIO). The distribution and chemical composition of aerosols over the NIO, are significantly impacted by the continental outflow, which is a blend of natural and anthropogenic aerosols in different seasons. To evaluate their influence on the surface water biogeochemical processes over the NIO, it is imperative to delineate the seasonal and spatial variations in aerosol composition. Such variations in chemical composition of aerosols over the oceanic regions, poses considerable challenges, primarily due to the absence of a static location and several logistic issues associated with cruise-based campaigns. Consequently, alternative methodologies such as satellite observations or global reanalysis datasets emerge as promising tools to overcome these challenges. However, to ensure the accuracy and reliability of the results, it is important to validate these global models against real-time observations. In this study, we evaluated performance of global reanalysis models Copernicus Atmosphere Monitoring Service (CAMS) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) concerning long-term data derived from cruise-based collections and observations in the Arabian Sea (AS) and Bay of Bengal (BoB). Aerosol samples (PM10) were collected on-board cruise campaigns (n =10) in the AS (7) and BoB (3) in different seasons. Chemical composition of aerosols were measured in the collected samples and total mass load was estimated using different species following Aswini et al., (2022). The distributions of PM10, sulfate, dust and sea-salt were seen to be qualitatively similar between CAMS and MERRA-2 over the NIO, but their concentrations exhibit significant differences. The variability in sulfate and sea-salt concentration over the BoB was well captured (e.g., r = ~0.8 for sulfate and r = ~0.79 for sea-salt) in contrast to the dust and PM10. Similar performance is seen with sulfate and sea-salt over the AS (with relatively lower ~ r = 0.4). Notably, the model shows a stronger limitation in reproducing high-dust (> 10 μg/m3) conditions over the BoB as well as the AS for MERRA-2. CAMS also shows a poor correlation with the measured dust in all the scenarios (r = 0.07). In all the scenarios as well as regions, it was found that best estimation by any of the two models were reported for the sulfate and sea-salt. The poor representation of dust (and PM10) by the model, over this region, could be possibly due to least constrained dust sources and their emission pattern. Our study suggests that reanalysis data from MERRA-2 and CAMS require further more rigorous validation with online monitoring stations over the oceanic region before taking into use in the estimation of air quality monitoring. REFERENCE Aswini, M. A., Tiwari, S., Singh, U., Kurian, S., Patel, A., Gunthe, S. S., & Kumar, A. (2022). Aeolian dust and sea salt in marine aerosols over the Arabian Sea during the southwest monsoon: Sources and spatial variability. ACS Earth and Space Chemistry, 6(4), 1044-1058.