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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 aerosol properties and the choice of different "preindustrial" reference years prevent a concise understanding of 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 instead of single aerosol properties from preindustrial times to the year 2050 under three different Shared Socioeconomic Pathway (SSP) scenarios. The clustering is based on a combination of statistic-based machine learning algorithms and output from emissions-driven global aerosol model simulations, which do not consider the effects of climate change. 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, where 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 preindustrial and present-day conditions, 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 resemble preindustrial conditions, where 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 conditions. Further analysis indicates a north–south difference in the clean background regime during preindustrial times and close resemblance of preindustrial aerosol conditions in the marine regime to present-day conditions in the Southern Hemispheric ocean. Not considering the effects of climate change is expected to cause uncertainties in the size and extent of the identified aerosol regimes but not the general regime patterns. This is due to a dominating influence of emissions rather than climate change in most cases. 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]

Atmospheric aerosols can act as ice-nucleating particles (INPs) and influence the formation and the microphysical properties of cirrus clouds, resulting in distinct climate effects. We employ a global aerosol-climate model, including a twomoment cloud microphysical scheme and a parametrization for aerosol-induced ice formation in cirrus clouds, to quantify the climate impact of INPs on cirrus clouds. The model considers mineral dust, (aviation) soot, crystalline ammonium sulfate, and glassy organics as INPs in the cirrus regime. A number of sensitivity experiments are performed to analyse various aspects of the simulated INP-cirrus effect regarding (i) the ice-nucleating potential of the INPs, (ii) the inclusion of ammonium sulfate and organic particles as INPs in the model, and (iii) the model representations of vertical updrafts. The resulting global radiative forcing of the total INP-cirrus effect, considering all different INP-types, assuming a smaller and a larger ice nucleating potential of INPs, is simulated as -28 and -55mWm-2, respectively. While the simulated impact of glassy organic INPs is mostly small and not statistically significant, ammonium sulfate INPs contribute a considerable radiative forcing, which is nearly as large as the combined effect of mineral dust and soot INPs. Additionally, the anthropogenic INP-cirrus effect is analysed considering the difference between present-day (2014) and pre-industrial conditions (1750) and amounts to -29mWm-2. In an additional sensitivity experiment we analyse the effect of highly efficient INPs proposed for cirrus cloud seeding as a means to reduce global warming by climate engineering. However, the results indicate that this approach risks an overseeding of cirrus clouds and often results in positive radiative forcings. Idealized experiments with prescribed vertical velocities highlight the crucial role of the model dynamics for the simulated INP-cirrus effects, e.g. resulting forcings increase about one order of magnitude when increasing the prescribed vertical velocity. The large discrepancy in the magnitude of the simulated INP-cirrus effect between different model studies emphasizes the need for future detailed analyses and efforts to reduce this uncertainty and constrain the resulting climate impact of INPs. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols can act as ice nucleating particles (INPs) and thereby influence the formation and the micro-physical properties of cirrus clouds resulting in distinct climate modifications. From laboratory experiments several types of aerosol particles have been identified as effective INPs at cirrus conditions. However, the global atmospheric distribution of INPs in the cirrus regime is still highly uncertain as in situ observations are scarce and limited in space and time. To study the influence of INPs on cirrus clouds and climate on the global scale these particles have been simulated with global chemistry-climate models. Typically, mineral dust and soot particles, which are known to initiate ice nucleation in cirrus clouds, have been considered in these models. In addition, laboratory studies suggest crystalline ammonium sulfate and glassy organic particles as effective INPs in the cirrus regime. However, the representation of these particles in global models is challenging as their phase state, i.e. crystalline or glassy, needs to be simulated. In turn, crystalline ammonium sulfate and glassy organics have only rarely been considered in global model studies and their impact on the global scale is still uncertain. Here, we present and analyse a global climatology of INPs derived from global model simulations performed with the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model including the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) coupled to a two-moment cloud microphysical scheme and a parametrization for aerosol-induced ice formation in cirrus clouds. This global INP-climatology comprises mineral dust and soot particles, as well as crystalline ammonium sulfate and glassy organics, including a simplified formulation of the particle phase state for the latter. By coupling the different INP-types to the microphysical cirrus cloud scheme, their ice nucleation potential at cirrus conditions is analysed, considering possible competition mechanisms between different INPs. The simulated INP concentrations in the range of about 1 to 100 L-1 agree well with in situ observations and other global model studies. We show that INP concentrations of glassy organics and crystalline ammonium sulfate are strongly related to the ambient conditions which often inhibit the glassy or crystalline phase, respectively. Our model results suggest that glassy organic particles probably have only minor influence, as typical INP concentrations are mostly low in the cirrus regime. On the other hand, crystalline ammonium sulfate often shows large INP concentrations, has the potential to influence ice nucleation in cirrus clouds, and should be taken into account in future model applications. [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]

Mineral dust particles play an important role in the climate system, by e.g. interacting with solar and terrestrial radiation or facilitating the formation of cloud droplets. Additionally, dust particles can act as very efficient ice nuclei in cirrus clouds. Many Global Chemistry Climate Models (GCCMs) use prescribed monthly mean mineral dust emissions representative of a specific year, based on a climatology. It was hypothesized that using dust emission climatologies may lead to misrepresentations of strong dust burst episodes, resulting in a negative bias of model dust concentrations compared to observations for these episodes. Here, we apply the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) as part of the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model. We employ two different representations of mineral dust for our model simulations: i) a prescribed monthly-mean climatology of dust emissions representative of the year 2000; ii) an online dust parametrization which calculates wind-driven mineral dust emissions at every model time-step. We evaluate model results for these two dust representations by comparison with observations of aerosol optical depth from ground-based station data. The model results show a better agreement with the observations for strong dust burst events when using the online dust representation compared to the prescribed dust emissions setup. Furthermore, we analyse the effect of increasing the vertical and horizontal model resolution on mineral dust properties in our model. The model is evaluated against airborne in situ measurements performed during the SALTRACE mineral dust campaign (Saharan Aerosol Long-range Transport and Aerosol-Cloud Interaction Experiment, June/July 2013), i.e. observations of dust transported from the Sahara to the Caribbean. Results show that an increased horizontal and vertical model resolution is able to better represent the spatial distribution of airborne mineral dust, especially in the upper troposphere (above 400 hPa). Additionally, we analyse the effect of varying assumptions for the size distribution of emitted dust. The results of this study will help to identify the model setup best suited for future studies and to further improve the representation of mineral dust particles in EMAC-MADE3. [ABSTRACT FROM AUTHOR]