Clouds are known to be important sinks of aerosol particles in the atmosphere, while aerosols in turn are known to influence the properties and climate impacts of clouds. However, the processes controlling the net effects of clouds on aerosol particle populations are still poorly understood and the descriptions in models are often rudimentary. Clouds can also facilitate new particle formation (NPF), in particular in the free troposphere. In this project, the micro- and macrophysical processes governing the net effects of clouds on aerosol particle populations and their climate effects will be studied. The project will consist of essentially three parts: 1) hypothesis building using novel machine learning tools for contextual analysis of existing research articles and available physics models, focusing on interactions between clouds and aerosol particles in the context of global climate (collabo- ration between Iris.ai and SU); 2) testing of the selected hypotheses using e.g. the ACDC code for simulating new particle formation, LES modelling tool MIMICA; 3) using network analysis techniques (secondment to EPFL) to identify signals of cloud effects on aerosol loadings in global model simulations and observational data (using data available e.g. through the GASSP database).
Ilona Riipinen (supervisor), Miguel Rodrigues (co-supervisor), Victor Botev (IRIS AI, non-academic advisor)
7 months at Iris.ai, 2 months at The Alan Turing Institute & 3 months at EPFL
Enrolment in Doctoral degree
PhD in Environmental Science at Stockholm University