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  • Project description

    Atmospheric aerosol particles are of great importance for cloud formation in the atmosphere because they are needed to act as cloud condensation nuclei (CCN) in liquid-water clouds and as ice nucleating particles (INP) in ice-containing clouds. Changes in aerosol concentration affect the albedo, development, phase, lifetime and rain rate of clouds. These aerosol-cloud interactions (ACI) and the resulting climate effects have been in the focus of atmospheric research for several decades. Nevertheless, the IPCC still concludes that ACI cause the largest uncertainty in assessing climate change as they are understood only with medium confidence.

    PACIFIC will improve our understanding of ACI by enhancing the representation of the aerosols relevant for cloud processes and by quantifying temporal changes in cloud properties throughout the cloud life cycle. ACI studies using polar-orbiting sensors are limited to snap-shot observations of clouds. CCN concentrations for assessing ACI are currently estimated from column- integrated optical aerosol parameters. There is no such proxy of INP concentrations for remote-sensing studies of aerosol effects on cold clouds as INP activity depends on aerosol type and size. Quantifying the role of aerosols in ACI requires knowledge of the spatial and vertical distribution of CCN and INP. I will use my experience in advancing state-of-the-art lidar retrievals to obtain unprecedented insight in CCN and INP concentrations from spaceborne lidar data. In addition, I will characterise the development of clouds before and after the snap-shot view of polar-orbiting sensors by tracking those clouds in time-resolved geostationary observations. This novel information will be used to study the effects of CCN and INP on the albedo, liquid and ice water content, droplet and crystal size, development, phase and rain rate of clouds within different regimes carefully accounting for the meteorological background. The findings of PACIFIC are crucial for assessing and improving the performance of climate models.

    Last update on 13 August 2019 by M. Tesche