Event Calendar

In this talk, I will provide a brief introduction to two main research topics that I am currently working on, which involve integrating observation, machine learning, and numerical models to enhance our understanding of aerosol-cloud interactions.
 
The first topic explores how semi-volatile compounds affect aerosol activation into cloud droplets on a global scale. In my PhD, we found that these semi-volatile compounds could halve the critical supersaturation for cloud formation, as calculated from Köhler theory, in Delhi, India - an effect not yet incorporated into current models. Currently, there is no direct evidence of co-condensation effect under supersaturated conditions. Our team at ETH designed smog chamber experiments to quantify this co-condensation effect under subsaturated conditions and developed a cloud parcel model to simulate it, with the aim of parameterising it in global models. I would like to discuss the feasibility of conducting co-condensation experiments in a cloud chamber setting.
 
A second focus of my research is understanding the impacts of aerosols impact on clouds using satellite data and machine learning. In a recent collaboration, we used volcanic degassing events as natural experiments, combing machine learning with satellite data to disentangle aerosols’ impact on clouds from confounding meteorology on a large scale. This provides a robust observational benchmark for climate models. I will introduce the intercomparison of this constraint with ECHAM6-HAM2 and CESM2 model simulations, to explore how we can further improve these models.

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The turbulent mixing of clouds with their environment decreases cloud amount, and hence the ability of clouds to reflect solar radiation. Because the mixing increases with the droplet and hence aerosol concentration, this interaction is known to affect the role of clouds in the climate system, although its magnitude is hard to constrain. In this talk, I will review several mechanisms by which the droplet concentration affects the turbulent mixing of clouds and their environment, and discuss some associated problems. Ultimately, I will use ensembles of large-eddy simulations to derive a heuristic model by which the effect of aerosol-mediated mixing on clouds can be assessed. 

At temperatures between -40°C and 0°C, clouds can be mixed phase, so called because they consist of a mixture of both liquid cloud droplets and ice crystals. This type of cloud is especially poorly represented in climate models. One of the reasons is that both hydrometeors are assumed to be homogeneously mixed in global models, but observations show that ice and liquid are heterogeneously mixed and exist in separate "pockets". This difference in the 3-dimensional spatial distribution of ice and liquid is important to assess and quantify precipitation, cloud processes, radiative properties, and consequently their impact on climate change. The present study aims to better characterize mixed phase clouds and especially the spatial distribution of the thermodynamic phase and understand how meteorology, air parcel transport and aerosols impact it.
 
We defined a parameter to describe the spatial distribution of liquid and ice phases within mixed-phase clouds from observations from the space-based lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarisation). We spatially and temporally collocated the satellite measurements with reanalysis retrievals of aerosol concentration and meteorological parameters from ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) and MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2) and then applied a multi-linear linear regression fit to quantify the influence of the external parameters on the spatial distribution of the cloud phase up to first order. A second part of the study focuses on ground-based measurements from the North Slope Alaska Station (NSA), where the transport of air parcels is analysed according to cloud type.
 
Focusing on the Arctic region, the results show that temperature is the most important parameter influencing the liquid-ice interface: for example, clouds with a temperature above 265 K have seven times more liquid-ice interfaces and are more homogeneously mixed than clouds with a temperature below 253 K. Black carbon concentration are also important parameters to describe the phase distribution. At NSA, clouds associated with higher transport may be more heterogeneously mixed. The results could be used to refine the parameterisation of clouds in models and their impact on climate change. 

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