Climate and climate change, but also weather depends to a certain degree on the composition of the atmosphere, including gaseous and particular trace species and hydrometeors. Consequently, our workgroup focusses on the composition of the atmosphere and how it influences clouds, weather and climate. We are using a dual methodological approach:

• We are developing and applying numerical models of the atmosphere and the Earth System to investigate topics of weather analysis & forecasting and climate & climate change from the local to the global scale.
• We are developing and applying instruments for aerosol and cloud research. This includes both aircraft as well as balloon borne analysis of the atmosphere from the surface to the upper atmosphere. Additionally, we are interested in remote sensing of cloud and aerosol parameters.

We focus on three research topics using the two above mentioned approaches:

• Atmospheric (multiphase) chemistry:
• We conduct gas and multiphase chemistry simulations from box models to the global scale.
• We investigate the impact of radiatively active species (gases and aerosols) on weather and climate.
• We measure and analyse aerosol particles and hydrometeors in situ and with remote sensing devices..

• Atmospheric convection:
• We conduct model experiments with simulations of individual clouds up to parameterised convection on the global scale.
• We investigate the role of convective transport and scavenging in the vertical redistribution of trace species with models and observations.
• We investigate cloud properties from remote sensing data.

• Holistic Earth System Modelling:
• We conduct model experiments with a focus of interactions of the atmosphere with the terrestrial biosphere and human-atmosphere interactions.

Big Data in Atmospheric Physics (BINARY) is an interdisciplinary project, involving the research fields Atmospheric Physics and Computer Sciences. Researcher from the Institutes of Atmospheric Physics and Computer Sciences at the Johannes Gutenberg University Mainz investigate important scientific questions in Atmospheric Physics applying modern machine learning methods for big data sets.

Projections of climate change rely on an adequate representation of UTLS processes and their feedbacks in climate models. In the Collaborative Research Centre TPChange this will be addressed by a combination of field measurements, laboratory studies, theoretical approaches, and multiscale numerical modelling. Based on an improved understanding of relevant processes at different scales, we will develop parameterisations to improve state-of-the-art climate models. Our goal is to specify the impact of UTLS processes on composition, dynamics and ultimately on future climate and climate variability.

For general inquiries regarding open research positions please contact Prof. Tost (tosth@uni-mainz.de)