Cirrus clouds are composed mainly of ice crystals, many of which form on dust and other particles which have been lofted to high altitudes. To better understand how different particles contribute to the formation of ice crystals in these clouds, scientists have attempted to recreate the temperature and humidity conditions in which they form in controlled laboratory experiments. A key research challenge is to translate the uncertain results of those experiments to climate models, where one could try to estimate how changes in the amount and type of particles in the atmosphere impact clouds and climate. The goal of this study was to explore how the range of uncertainty we derive from experimental results influences our estimates of how much these particles warm or cool the climate.

The study found that depending on just how strong the connection one estimates between particles and their ability to form ice crystals, increasing the amount of these particles could lead to either climate warming or cooling. Because many different experimental and modeling setups yield different estimates of the strength of this particle-ice connection, it is critical that the climate science community work to better fine-tune its measurements of this important physical process, and thoroughly account for the diversity of estimates when studying aerosol impacts on climate.

This study investigates the measurement of ice nucleating particle (INP) concentrations and sizing of crystals using continuous flow diffusion chambers (CFDCs). CFDCs have been deployed for decades to measure the formation of INPs under controlled humidity and temperature conditions in laboratory studies and by ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in models by relating the formation of ice crystals to state variables such as temperature and humidity as well as aerosol particle properties such as composition and number. We show here that assumptions of ideal instrument behavior are not supported by measurements made with a commercially available CFDC, the SPectrometer for Ice Nucleation (SPIN), and the instrument on which it is based, the Zurich Ice Nucleation Chamber (ZINC). Non-ideal instrument behavior, which is likely inherent to varying degrees in all CFDCs, is caused by exposure of particles to different humidities and/or temperatures than predicated from instrument theory of operation. This can result in a systematic, and variable, underestimation of reported INP concentrations. We find here variable correction factors from 1.5 to 9.5, consistent with previous literature values. We use a machine learning approach to show that non-ideality is most likely due to small-scale flow features where the aerosols are combined with sheath flows. Machine learning is also used to minimize the uncertainty in measured INP concentrations. We suggest that detailed measurement, on an instrument-by-instrument basis, be performed to characterize this uncertainty.