A011-0002
The Influence of Anthropogenic Aerosols on Cirrus Clouds Determined from In-Situ Observations and NCAR CAM6 Data

Monday, 7 December 2020
Poster
Flor Vanessa Vanessa Maciel and Minghui Diao, San Jose State University, Meteorology and Climate Science, San Jose, CA, United States
Abstract:
Cirrus clouds are a significant part of the upper atmosphere that can have either a dampening or amplifying effect upon global warming. Due to their complexity, the parametrizations used for them within climate models can produce varying outcomes that may not reflect reality. Furthermore, anthropogenic aerosols can further complicate their parametrization because of aerosol indirect effects. Thus, it is important to further investigate this relationship between cirrus clouds and atmospheric aerosols.

In-situ observational data from seven NSF-funded flight campaigns were utilized in this project and compared with simulated data from the National Center for Atmospheric Research Community Atmosphere Model 6 (NCAR CAM6) to help inform this relationship. It was found previously that the controlling factors, temperature, relative humidity with respect to ice, vertical velocity and aerosol number concentrations, all play a key role in affecting ice microphysical properties (Patnaude and Diao, 2020). Yet, it is unclear how different model parametrizations can provide the optimal results that match closely with the observed microphysical properties under various conditions. Thus, this work builds upon that past research by introducing different cloud parametrizations and ice nucleation mechanisms (i.e., homogeneous and heterogeneous nucleation) in order to discern which combinations match best with the observational data.

As climate change progresses, we must continue to study cirrus clouds and their relationship with aerosols in order to fully capture their effects in a model environment that reflects observations closely and accurately. Society will benefit most if climate models produce accurate and reproducible results. Thus, this research hopes to help advance models to the next iteration necessary to do so.