Ice‐Nucleating Particles That Impact Clouds and Climate: Observational and Modeling Research Needs. (16th May 2022)

• Record Type: Journal Article
• Authors: Burrows, Susannah M.; McCluskey, Christina S.; Cornwell, Gavin; Steinke, Isabelle; Zhang, Kai; Zhao, Bin; Zawadowicz, Maria; Raman, Aishwarya; Kulkarni, Gourihar; China, Swarup; Zelenyuk, Alla; DeMott, Paul J.
• Is Part Of: Reviews of geophysics. Volume 60:Number 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-16
• Subjects: ice nucleation -- ice nucleating particles -- observation‐model integration; Geophysics -- Periodicals; 550.5
• DOI: 10.1029/2021RG000745
• Languages: English
• ISSNs: 8755-1209

Abstract

Abstract: Atmospheric ice‐nucleating particles (INPs) play a critical role in cloud freezing processes, with important implications for precipitation formation and cloud radiative properties, and thus for weather and climate. Additionally, INP emissions respond to changes in the Earth System and climate, for example, desertification, agricultural practices, and fires, and therefore may introduce climate feedbacks that are still poorly understood. As knowledge of the nature and origins of INPs has advanced, regional and global weather, climate, and Earth system models have increasingly begun to link cloud ice processes to model‐simulated aerosol abundance and types. While these recent advances are exciting, coupling cloud processes to simulated aerosol also makes cloud physics simulations increasingly susceptible to uncertainties in simulation of INPs, which are still poorly constrained by observations. Advancing the predictability of INP abundance with reasonable spatiotemporal resolution will require an increased focus on research that bridges the measurement and modeling communities. This review summarizes the current state of knowledge and identifies critical knowledge gaps from both observational and modeling perspectives. In particular, we emphasize needs in two key areas: (a) observational closure between aerosol and INP quantities and (b) skillful simulation of INPs within existing weather and climate models. We discuss the state of knowledge on various INP particle types and briefly discuss the challenges faced in understanding the cloud impacts of INPs with present‐day models. Finally, we identify priority research directions for both observations and models to improve understanding of INPs and their interactions with the Earth System. Plain Language Summary: Atmospheric ice‐nucleating particles (INPs) are rare particles that play a critical role in enabling ice crystals to form in clouds. Ice crystals in clouds act as seeds for most precipitation that reaches the Earth's surface, and impact climate by changing the amount of sunlight clouds reflect. A variety of naturally occurring particle sources contribute to atmospheric INPs, including wind‐blown dusts, sea spray particles, biological particles such as fungal spores, bacteria and pollen, and ash and other particles from forest fires. Human‐caused particulate pollution may also contribute to atmospheric INPs in some circumstances. A better understanding of these particles, including their sources to the atmosphere, their interactions with atmospheric processes, and their impacts on clouds, is therefore required to improve climate predictability in the coming century. While impressive progress has been achieved in recent years in process‐level understanding of INPs and their cloud impacts, progress in this area will require better integration between observational and modeling tools and perspectives. This review discusses the current state of knowledge regarding INPs, key observational and modeling gaps, and identifies priority research areas that emphasize a unified effort between measurement and modeling communities. Key Points: Atmospheric ice‐nucleating particles (INPs) play a critical role in weather and climate by facilitating ice formation in clouds This review summarizes current knowledge on observational constraints, modeling, and cloud impacts of INPs Research priorities are identified to both advance fundamental understanding and bridge the observation‐model gap for INPs