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Aerosols play an important role in the Earth system, but their impact on cloud properties and the resulting radiative forcing of climate remains highly uncertain. The large temporal and spatial variability of a number of aerosols properties and the choice of different 'pre-industrial' reference years prevent a concise understanding of basic underlying patterns and trends in aerosols and their impacts on clouds and radiation. In this study, we characterize the spatial patterns and long-term evolution of lower tropospheric aerosols (in terms of regimes) by clustering multiple aerosol properties from preindustrial times to the year 2050 under three different Shared Socioeconomic Pathway (SSP) scenarios, based on a combination of global aerosol model simulations and statistic-based machine learning algorithms. Our analysis suggests that in comparison with the present-day case, lower tropospheric aerosol regimes during preindustrial times are mostly represented by regimes of comparatively clean conditions whereby marked differences between the years 1750 and 1850 emerge due to the growing influence of agriculture and other anthropogenic activities in 1850. Key aspects of the spatial distribution and extent of the aerosol regimes identified in year 2050 differ compared to pre-industrial and present-day, with significant variations resulting from the emission scenario investigated. In 2050, the low emission SSP1-1.9 scenario is the only scenario where the spatial distribution and extent of the aerosol regimes very closely resembles preindustrial conditions whereby the similarity is greater compared to 1850 than 1750. The aerosol regimes for 2050 under SSP3-7.0 closely resemble present-day conditions, but there are some notable regional differences: developed countries tend to shift towards cleaner conditions in future, while the opposite is the case for developing countries. The aerosol regimes for 2050 under SSP2-4.5 represent an intermediate stage between preindustrial times and present-day. Further analysis indicates a north/south difference in the background regime during preindustrial times, and close resemblance of pre-industrial aerosol conditions in the marine regime to present-day conditions in the Southern Hemispheric ocean. Overall, our study allows to extract a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios and to summarize these in terms of aerosol regimes. The approach and findings of this study can be used for designing targeted measurements of different preindustrial-like conditions, and for tailored air pollution mitigation measures in specific regions. [ABSTRACT FROM AUTHOR]

A machine learning K-means algorithm is applied to data of seven aerosol properties from a global aerosol simulation using EMAC-MADE3. The aim is to partition the aerosol properties across the global atmosphere in specific aerosol regimes. K-means is an unsupervised machine learning method with the advantage that an a priori definition of the aerosol classes is not required. Using K-means, we are able to quantitatively define global aerosol regimes, so-called aerosol clusters, and explain their internal properties as well as their location and extension. This analysis shows that aerosol regimes in the lower troposphere are strongly influenced by emissions. Key drivers of the clusters’ internal properties and spatial distribution are, for instance, pollutants from biomass burning/biogenic sources, mineral dust, anthropogenic pollution, as well as their mixing. Several continental clusters propagate into oceanic regions. The identified oceanic regimes show a higher degree of pollution in the northern hemisphere than over the southern oceans. With increasing altitude, the aerosol regimes propagate from emission-induced clusters in the lower troposphere to roughly zonally distributed regimes in the middle troposphere and in the tropopause region. Notably, three polluted clusters identified over Africa, India and eastern China, cover the whole atmospheric column from the lower troposphere to the tropopause region. A markedly wide application potential of the classification procedure is identified and further aerosol studies are proposed which could benefit from this classification. [ABSTRACT FROM AUTHOR]