Cirrus clouds have important effects on Earth’s radiation budget because of their large global coverage and their high formation altitude. They have opposing radiative effects by reflecting solar radiation back to space (cooling) and trapping infrared radiation similarly to greenhouse gases (warming). The mechanism of ice nucleation on aerosols and the distribution of ice in clouds can influence global precipitation, which in turn affects the surface energy balance and the hydrologic cycle. Conventional global climate models (GCMs) have substantial difficulty in simulating the evolution of cirrus clouds because many of their driving dynamic processes are not explicitly resolved with the relatively coarse spatial and temporal model resolutions of these GCMs. A team of scientists, including Department of Energy researchers at Pacific Northwest National Laboratory, implemented an aerosol-dependent ice nucleation scheme into an aerosol-enabled multi-scale modeling framework (MMF) to study ice formation in cirrus clouds. The newest version of the PNNL-developed MMF model represents cloud scale processes by embedding a cloud-resolving model (CRM) within each vertical column of a GCM grid. They found that the MMF model predicts a higher frequency of mid-latitude super-saturation in the southern and winter hemisphere, which is consistent with previous satellite and in-situ observations. Compared to a conventional GCM, this research showed that the MMF is a more powerful model to simulate parameters that evolve over short time scales, such as super-saturation. The research shows that the MMF is inherently better suited to simulate processes with a short time scale (less than that of typical GCM time steps), such as ice nucleation.