A Physically-Based Heterogeneous Ice nucleation Parameterization in CAM5
Mixed-phase clouds with both liquid and ice coexisting play a critical role in modulating the Earth’s radiation flux, and precipitation formation. The production of ice crystals through ice nucleation can significantly influence the mixed-phase cloud properties, and their lifetime and persistence. In the Community Atmospheric Model version 5 (CAM5), the default heterogeneous ice nucleation scheme in mixed-phase clouds is based on Meyers et al. (1992), which is an empirical parameterization and does not have a link to aerosol characteristics. We developed and implemented a physically-based heterogeneous ice nucleation scheme in CAM5 to replace the Meyers et al. (1992) parameterization. The new scheme is based on the classical nucleation theory (CNT) with two contact angle distributions (one is the single contact angle (α) distribution and the other is probability density function (PDF) of α distribution). The new scheme describes all important ice nucleation mechanisms on dust and soot (i.e., immersion/condensation, deposition, and contact freezing). Uncertain parameters in the new scheme are constrained by recent Saharan natural dust and soot observation datasets. We find when comparing with observations, the IN calculated by the a-PDF approach has a better agreement than that calculated by the single-aapproach at warm temperatures (T > -20°C). More ice crystals can form at lower altitudes (with warmer temperatures) simulated by the a-PDF approach compared with the single-aapproach in CAM5. All of these can be attributed to different ice nucleation efficiencies among aerosol particles with some particles having smaller contact angles (higher efficiencies) in the a-PDF approach. Both the single aand the a-PDF approaches indicate that the immersion freezing of natural dust plays a more important role in the heterogeneous nucleation than that of soot in mixed-phase clouds.
We would like to thank P. J. DeMott for providing the CFDC data shown in Figs. 10 and 11. This research was supported by the Office of Science of the US Department of Energy (DOE) as part of the Earth System Modeling Program. We would like to acknowledge the use of computational resources (ark:/85065/d7wd3xhc) at the NCAR-Wyoming Supercomputing Center provided by the National Science Foundation and the State of Wyoming, and supported by NCAR’s Computational and Information Systems Laboratory. C. Hoose acknowledges funding by the Helmholtz Association through the President’s Initiative and Networking Fund and by the Deutsche Forschungsgemeinschaft through project FOR 1525. We would like to thank the anonymous reviewers very much for their helpful suggestions.