The process of new particle formation from gas-phase precursors holds significant importance in Earth's atmosphere and introduces a notable source of uncertainty in climate change predictions. Typical conditions for new particle formation are moderate temperatures, clear sky and low background aerosol contamination. This general paradigm was challenged by the puzzling observation of frequent new particle formation in megacities. The pre-existing aerosol loadings in such environments seemed to be too high to allow clusters of being formed and grow fast enough before they encounter a collision with a background particle and get lost from the number budget.
Here, we show how nanoparticle growth in urban atmospheres is facilitated enabling efficient survival of nanoclusters providing an explanation for the occurrence of NPF in heavily polluted environments. We outline the tool set, which we have developed over the recent years to address this puzzle. Significant uncertainty in the particle number size distribution measurements and growth rate estimates were addressed through new instrumentation and analysis approaches. At the same time, we refined growth models to account for the challenges of a wide variety of potentially condensable vapors and updated our understanding of particle survival in the atmosphere.
We could demonstrate that new particle formation takes a decisive role in air quality issues in megacities, especially as nanoparticles seem to grow at surprisingly constant rates even when no new particle formation is observed. The “unique atmospheric experiment” of the Covid-19 lockdowns finally provided the chance to estimate how sensitive the urban environment is to changes in the atmospheric chemistry, especially with respect to new particle formation. While we speculated that other condensable vapors than previously thought could be part of the puzzle, we can finally show that also the population dynamics are crucial for more efficient nanoparticle survival than previously thought. However, severe challenges remain, as the outlined methodological improvements also revealed that sometimes the little ones even grow slower than expected.
How to cite: Stolzenburg, D.: How do the little ones grow? Solving the puzzling occurrence of new particle formation in megacities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2853, https://doi.org/10.5194/egusphere-egu25-2853, 2025.
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.
How to cite: Lohmann, U.: From the microscale to climate: combining observations, laboratory data, and numerical simulations for aerosol-cloud interactions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5678, https://doi.org/10.5194/egusphere-egu25-5678, 2025.
