Atmospheric Composition Modeling and Analysis Program

Fanourgakis, G.S., et al. (2019), Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation, Atmos. Chem. Phys., 19, 8591-8617, doi:10.5194/acp-19-8591-2019.

Zhao, X., et al. (2018), Using Long-Term Satellite Observations to Identify Sensitive Regimes and Active Regions of Aerosol Indirect Effects for Liquid Clouds Over Global Oceans, J. Geophys. Res., 123, 457-472, doi:10.1002/2017JD027187.

Yu, F., et al. (2017), Impact of temperature dependence on the possible contribution of organics to new particle formation in the atmosphere, Atmos. Chem. Phys., 17, 4997-5005, doi:10.5194/acp-17-4997-2017.

Luo, G., et al. (2020), Further improvement of wet process treatments in GEOS-Chem v12.6.0: impact on global distributions of aerosols and aerosol precursors, Geosci. Model. Dev., 13, 2879-2903, doi:10.5194/gmd-13-2879-2020.

Luo, G., et al. (2019), Revised treatment of wet scavenging processes dramatically improves GEOS-Chem 12.0.0 simulations of surface nitric acid, nitrate, and ammonium over the United States, Geosci. Model. Dev., 12, 3439-3447, doi:10.5194/gmd-12-3439-2019.

Prenni, A.J., et al. (2019), An examination of the algorithm for estimating light extinction from T IMPROVE particle speciation data, Atmos. Environ., 214, 116880, doi:10.1016/j.atmosenv.2019.116880.

Nair, A.A., and F. Yu (2020), Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements, Atmos. Chem. Phys., doi:10.5194/acp-2020-509.

Jia, H., et al. (2020), Distinct Impacts of Increased Aerosols on Cloud Droplet Number Concentration of Stratus/Stratocumulus and Cumulus, Geophys. Res. Lett..

Yu, F., et al. (2018), Long-Term Trend of Gaseous Ammonia Over the United States: Modeling and Comparison With Observations, J. Geophys. Res., 123, 8315-8325, doi:10.1029/2018JD028412.