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Predicted Particle Properties

Overview

The stratiform cloud microphysics scheme in v3 is Predicted Particle Properties (P3; Morrison & Milbrandt, 2015;1 Milbrandt & Morrison, 20162). P3 offers a new approach to representing the evolution of ice particles that is more physical than the traditional approach of using predefined ice categories. It has been implemented in E3SM (Wang et al., 2021)3 to address the limitations in the original microphysics scheme- the lack of riming particles and the artificial conversion between ice crystals and snow particles. The current version in E3SM is a two-moment scheme with a single ice category (Morrison & Milbrandt, 2015).1 In addition to the total ice number and mass mixing ratios, P3 prognoses the rimed mass and rimed volume mixing ratios, which allows for the prediction of the continuous evolution of the rime fraction and particle density. It is worth noting that the ice nucleation parameterizations are changed to be aerosol-dependent in this study, with the heterogenous ice nucleation parameterizations from the Classical Nucleation Theory (Liu et al., 2012)4 and the homogenous in-situ cirrus formation based on Liu and Penner (2005).5 This differs from the P3 used in WRF and that used in the E3SM v1 in Wang et al. (2021)3 where the heterogeneous ice nucleation parameterizations are temperature dependent only.

Namelist parameters

P3 Namelist Parameters

  1. Hugh Morrison and Jason A. Milbrandt. Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests. Journal of the Atmospheric Sciences, 72(1):287–311, January 2015. URL: https://journals.ametsoc.org/view/journals/atsc/72/1/jas-d-14-0065.1.xml (visited on 2024-03-29), doi:10.1175/JAS-D-14-0065.1. ↩↩

  2. J. A. Milbrandt and H. Morrison. Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part III: Introduction of Multiple Free Categories. Journal of the Atmospheric Sciences, 73(3):975–995, March 2016. URL: https://journals.ametsoc.org/view/journals/atsc/73/3/jas-d-15-0204.1.xml (visited on 2024-03-29), doi:10.1175/JAS-D-15-0204.1. ↩

  3. Jingyu Wang, Jiwen Fan, Zhe Feng, Kai Zhang, Erika Roesler, Benjamin Hillman, Jacob Shpund, Wuyin Lin, and Shaocheng Xie. Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM. Journal of Advances in Modeling Earth Systems, 13(11):e2021MS002628, November 2021. URL: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021MS002628 (visited on 2024-03-29), doi:10.1029/2021MS002628. ↩↩

  4. X. Liu, R. C. Easter, S. J. Ghan, R. Zaveri, P. Rasch, X. Shi, J.-F. Lamarque, A. Gettelman, H. Morrison, F. Vitt, A. Conley, S. Park, R. Neale, C. Hannay, A. M. L. Ekman, P. Hess, N. Mahowald, W. Collins, M. J. Iacono, C. S. Bretherton, M. G. Flanner, and D. Mitchell. Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5. Geoscientific Model Development, 5(3):709–739, May 2012. URL: https://gmd.copernicus.org/articles/5/709/2012/ (visited on 2024-03-29), doi:10.5194/gmd-5-709-2012. ↩

  5. Xiaohong Liu and Joyce E. Penner. Ice nucleation parameterization for global models. Meteorologische Zeitschrift, pages 499–514, September 2005. URL: https://www.schweizerbart.de/papers/metz/detail/14/54281/Ice_nucleation_parameterization_for_global_models (visited on 2024-03-29), doi:10.1127/0941-2948/2005/0059. ↩