Studying the cloud-forming capacity of aerosols is crucial in climate research. The PartMC model can provide detailed particle information and help these studies. This model is integrated with the ideal meteorological Cloud Model 1 (CM1) to simulate the aerosols at cloud-forming locations. Significant changes are revealed in the hygroscopicity distribution of aerosols within ascending air parcels. Additionally, different ascent times also affect aerosol aging processes.

Biomass fires emit aerosols and precursors that can provide a novel environment for initiating stratospheric ozone loss. The Airborne Tropical TRopopause EXperiment campaign sampled the western Pacific, the dominant longitudes for surface air lofted by convection to enter the global stratosphere. Aircraft measurements over multiple flights revealed persistent layers of biomass burning pollutants entering the lower stratosphere and originating from fires as far away as Africa and Indonesia.

Brown carbon (BrC) is a type of airborne particle produced from various combustion sources which is light absorption. Historically, climate models have categorizing organic particles as either non-absorbing or purely reflective. Our study shows that BrC can reduce the usual cooling effect of organic particles. While BrC is often linked to biomass burning, however, BrC from fossil fuels contributes significantly to atmospheric heating.

Several recent studies have reported complete cloud glaciation induced by airborne-based glaciogenic cloud seeding over plains. Since turbulence is an important factor to maintain clouds in a mixed phase, it is hypothesized that turbulence may have an impact on the seeding effect. This hypothesis is evident in the present study, which shows that turbulence can accelerate the impact of airborne glaciogenic seeding of stratiform clouds.

This study analyzed particles in hailstones from Argentina to better understand hail formation and growth. A unique method was used that revealed the particles’ size, composition, and location within the hail, including a variety of particle sizes and compositions linked to local land uses, such as mountainous, agricultural, and urban areas. The findings highlight the potential impacts of natural and human-related factors on hail formation and provide a new method for studying hail globally.

This work comprises a comprehensive investigation into the chemical and optical properties of brown carbon (BrC) in PM_2.5 samples collected in Nanjing, China. In particular, we used a machine learning approach to identify a list of key BrC species, which can be a good reference for future studies. Our findings extend understanding of BrC properties and are valuable to the assessment of BrC's impact on air quality and radiative forcing.