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Abstract Ice‐nucleating particles (INPs) play a key role in ice formation and cloud microphysics and thus significantly impact the water cycle and the climate. However, our understanding of atmospheric INPs, particularly their sources, emissions, and spatiotemporal variability, is incomplete. While the enhancement of atmospheric INP concentrations with rainfall has been previously shown, a mechanistic understanding of the process is lacking. Here, we link detailed precipitation observations with near‐surface atmospheric INP concentrations at a semiarid grassland site in Colorado. Considering the during‐precipitation air samples, INP concentrations positively correlate with cumulative rainfall kinetic energy and amount, suggesting that INP aerosolization is induced by raindrop and hailstone impact. By additionally analyzing the INP content of precipitation water, terrestrial source samples, and heat‐treated samples, we demonstrate that local plants are the most plausible source of rain‐induced INPs during a precipitation event. Should INPs aerosolized by precipitation rise to cloud height, they could influence cloud ice fraction and initiate precipitation resulting in an aerosol‐cloud‐precipitation feedback. 

Stratocumulus clouds over the Southern Ocean have fewer droplets and are more likely to exist in the predominately supercooled phase than clouds at similar temperatures over northern oceans. One likely reason is that this region has few continental and anthropogenic sources of cloud-nucleating particles that can form droplets and ice. In this work, we present an overview of aerosol particle types over the Southern Ocean, including new measurements made below, in and above clouds in this region. These measurements and others indicate that biogenic sulfur-based particles >0.1 μm diameter contribute the majority of cloud condensation nuclei number concentrations in summer. Ice nucleating particles tend to have more organic components, likely from sea-spray. Both types of cloud nucleating particles may increase in a warming climate likely to have less sea ice, more phytoplankton activity, and stronger winds over the Southern Ocean near Antarctica. Taken together, clouds over the Southern Ocean may become more reflective and partially counter the region’s expected albedo decrease due to diminishing sea ice. However, detailed modeling studies are needed to test this hypothesis due to the complexity of ocean-cloud-climate feedbacks in the region. 

Abstract Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol–ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on collocated measurements of the ambient size-resolved and single-particle composition and INP number concentrations. The acquired particle data serve as input in several immersion freezing parameterizations, which are employed in cloud-resolving and climate models, for prediction of INP number concentrations. We discuss in detail one closure case study in which a front passed through the measurement site, resulting in a change of ambient particle and INP populations. We achieved closure in some circumstances within uncertainties, but we emphasize the need for freezing parameterization of potentially missing INP types and evaluation of the choice of parameterization to be employed. Overall, this closure pilot study aims to assess the level of parameter details and measurement strategies needed to achieve aerosol–ice formation closure. The closure approach is designed to accurately guide immersion freezing schemes in models, and ultimately identify the leading causes for climate model bias in INP predictions.