Southern Africa is particularly exposed to climate change, characterised in the region by a rapid increase in temperatures and in the frequency and intensity of droughts. Natural and anthropogenic atmospheric aerosols are a fundamental component of the regional and global climate system, due to their direct, semidirect and indirect effect on the radiative balance. Aerosols also contribute to social impacts related to the local climate, particularly through their effect on air quality in urban areas. In this context, while climate model simulations provide fairly reliable simulations of essential climate variables at the global level, the representation of local climate impacts is still affected by great uncertainties. In particular, there is still little usable information available on the evolution of atmospheric aerosols in future climate scenarios, on a global scale and in southern Africa. The understanding of the dynamics of the aerosols and its relationship with the atmospheric dynamics still represents an ongoing challenge for climate modelling. This project will investigate the role of atmospheric circulation in the aerosol dynamics, using innovative machine learning methodologies for the definition of atmospheric circulation features, in a region where the literature on the topic is very limited.

The ideal candidate should have a strong background in data analysis and statistics (analysis of probability distribution functions, uncertainties, etc.) and be familiar with the management of large datasets. He/she should have basic knowledge of atmospheric physics, climate dynamics and change. Programming skills and knowledge of machine learning techniques are a plus.

The research activity will be carried out in the CARISMA group at IUSS Pavia. The CARISMA team is composed by STEM and Social scientists studying climate change and its associated impacts and risk. The project will be co-supervised by Dr. Benjamin Pohl of the team Biogeosciences of the Université de Bourgogne, in Dijon, France. The candidate will spend 12 months in Dijon to implement the machine-learning-based classification of the atmospheric circulation in southern Africa.