From the microscale to climate: combining observations, laboratory data, and numerical simulations for aerosol-cloud interactions

Clouds are fascinating objects because of their myriad shapes and the optical phenomena that they cause. They are also scientifically challenging to understand because their formation and dissipation require knowledge about both the large-scale meteorological environment as well as about the details of cloud droplet and ice crystal formation on the microscale. While we have reduced the uncertainty in the radiative forcing of aerosol-cloud interactions over the last decades, the effect of climate change on clouds, precipitation forecasts and cloud dynamics still pose lots of open questions.

With the advancement of better in-situ and remote sensing instruments, unprecedented observations of clouds are now possible. Simultaneously, the increasing amount of computing power enables us to simulate clouds at increasingly finer scales over larger domains, making convection parameterizations obsolete and allowing us to resolve larger eddies. Cloud research is also being revolutionized by machine learning. We have used machine learning in combination with satellite data to disentangle the response of stratocumulus clouds to aerosol perturbations, for understanding how cirrus clouds respond to the presence of mineral dust as well as for classifying ice crystals down to aggregated monomer scale in in-situ measurements.

We have exploited these advancements in our CLOUDLAB project, where we employed cloud seeding technology to better our understanding of mixed-phase cloud processes: by releasing silver iodide-containing particles from uncrewed aerial vehicles in supercooled low stratus clouds over the Swiss plateau, we were able to observe and measure downstream ice crystals in a controlled way. From these measurements, we quantifed their diffusional growth rates, aggregation rates and riming rates. Additional high-resolution modeling supported the experiments and provided insights for weather forecasts and climate projections. The CLOUDLAB results can also be translated to the potential climate mitigation idea of thinning mixed-phase clouds.

I have great hope that the open questions in cloud research will be tackled by a combination of advanced measurement devices, AI-driven methods, and further advances in computing power enabling high-resolution modeling.