The impact of aerosols on cloud properties (e.g. droplet size, precipitation rate) and thus the climate system is an important mechanism in the climate system; however, present-day understanding is limited. The project proposed here will contribute to improvements in the understanding of processes, as well as regional diffenceres and patterns. Aerosol-cloud-interactions will be considered at various spatial and temporal scales, using a variety of spatial data sets. Using geostationary satellite data, cloud development will be analysed at a high temporal resolution (15 minute steps). A second project part aims at quantifying the impact of aerosol on cloud properties on regional and global scales using multi-variate statistical methods. Collaboration in method development and data interpretation is planned with the European Climate Monitoring Satellite Application Facility (CMSAF) project and professor Yong-Sang Choi, EWHA, Korea. The multi-scale approach pursued in the proposed project is expected to yield insights that may be of use in the improvement of climate projections.(DFG GEPRIS)

• In two publications (Fuchs et al. 2015 [link to], Schwarz et al. (2017) [link to]), we have investigated the transition zone between aerosols and clouds with satellite remote sensing. Depending on the satellite sensor used, between 10 and 20% of the satellite observations fall into this transition zone and are frequently discarded by studies analyzing aerosol-cloud interactions.
• In three publications, we investigate the roles of aerosols and meteorology on stratocumulus clouds in the Southeast Atlantic. We find that aerosol-cloud interactions are more pronounced in unstable environments (Andersen 2015 [link to]), and that cloud responses to synoptic-scale variability features subregional characteristics (Fuchs et al. 2017 [link to]), Fuchs et al. 2018 [link to]). 
• In two more publications, we analyze global patterns of aerosol-cloud interactions and find that clouds are particularly sensitive to aerosol perturbations at low aerosol concentrations (Andersen et al. 2016 [link to]), and quantify global aerosol-cloud sensitivities with neural networks to constrain the influence of meteorological covariation (Andersen et al. 2017 [link to]).