Publication Abstracts

Yu et al. 2022

Yu, F., G. Luo, A.A. Nair, K. Tsigaridis, and S.E. Bauer, 2022: Use of machine learning to reduce uncertainties in particle number concentration and aerosol indirect radiative forcing predicted by climate models. Geophys. Res. Lett., 49, no. 16, e2022GL098551, doi:10.1029/2022GL098551.

The radiative forcing of anthropogenic aerosols associated with aerosol?cloud interactions (RFaci) remains the largest source of uncertainty in climate prediction. The calculation of particle number concentration (PNC), one of the critical parameters affecting RFaci, is generally simplified in climate models. Here we employ outputs from long-term (30-years) simulations of a global size-resolved (sectional) aerosol microphysics model and a machine-learning tool to develop a Random Forest Regression Model (RFRM) for PNC. We have implemented the PNC RFRM in GISS-ModelE2.1 with a mass-based One-Moment Aerosol module, which is one of CMIP6 models. Compared to the default setting, the GISS-ModelE2.1 simulation based on RFRM reduces the changes of cloud droplet number concentration associated with anthropogenic emissions, and decreases the RFaci from -1.46 W/m2 to -1.11 W/m2. This work highlights a promising approach based on machine learning to reduce uncertainties of climate models in predicting PNC and RFaci without compromising their computing efficiency.

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BibTeX Citation

@article{yu01100l,
  author={Yu, F. and Luo, G. and Nair, A. A. and Tsigaridis, K. and Bauer, S. E.},
  title={Use of machine learning to reduce uncertainties in particle number concentration and aerosol indirect radiative forcing predicted by climate models},
  year={2022},
  journal={Geophys. Res. Lett.},
  volume={49},
  number={16},
  pages={e2022GL098551},
  doi={10.1029/2022GL098551},
}

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RIS Citation

TY  - JOUR
ID  - yu01100l
AU  - Yu, F.
AU  - Luo, G.
AU  - Nair, A. A.
AU  - Tsigaridis, K.
AU  - Bauer, S. E.
PY  - 2022
TI  - Use of machine learning to reduce uncertainties in particle number concentration and aerosol indirect radiative forcing predicted by climate models
JA  - Geophys. Res. Lett.
VL  - 49
IS  - 16
SP  - e2022GL098551
DO  - 10.1029/2022GL098551
ER  -

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