Testing our predictive understanding of aerosol particles serving as ice-nucleating particles
Knopf, Daniel — Stony Brook University
Area of research:
Our predictive understanding of how aerosol particles serve as ice-nucleating particles (INPs), leading to ice crystal formation, inadequately represents their impacts on cloud properties and precipitation in cloud-resolving and climate models. We conducted a collaborative pilot field campaign as a "closure" exercise, to quantify our current ability to predict INP number concentrations from measurements of ambient aerosol properties and existing immersion freezing parameterizations. We found that detailed physical and chemical characterization of the ambient aerosol was crucial for the success of INP prediction. While closure can be achieved when known INP types are present, we need to develop parameterizations for other INP types not presently represented, especially for soil-organics and biological particles, to approach fuller understanding and closure.
This pilot study was a first attempt to evaluate our ability for aerosol-INP closure by determining INP number concentrations from aerosol composition by applying laboratory-derived immersion freezing parameterizations. This exercise allowed for a better understanding of the necessary experimental requirements and associated uncertainties to achieve closure between aerosol particles and INP number concentrations. The results of this campaign guide long-term INP measurements and model developments of INP parameterizations. Lastly, the findings emphasize the need for more research regarding organic, biogenic, and biological particles acting as INPs.
Researchers conducted an aerosol-ice formation closure pilot study at the ARM Southern Great Plains observatory (AEROICESTUDY) in October 2019 using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. During this closure study, co-located online and offline measurements of the ambient size-resolved and single-particle composition and INP number concentrations were performed. Several immersion freezing parameterizations that are currently employed in cloud-resolving and climate models for prediction of INP number concentrations and which account for particle numbers, surface area, and freezing time were evaluated. Focusing on one case study, the researchers achieved closure in some circumstances within uncertainties, but the results emphasize the need for freezing parameterizations that include potentially missing INP types as well as evaluation of the choice of parameterization to be employed. A key outcome of this closure pilot study was a definition of the level of parameter details and measurement strategies needed to achieve aerosol-ice formation closure. This in turn improves immersion freezing parameterizations in models, and ultimately allows us to identify the leading causes for climate model bias in INP predictions.