Journal
GEOPHYSICAL RESEARCH LETTERS
Volume 48, Issue 21, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021GL094133
Keywords
Cloud condensation nuclei (CCN); particle size distribution (PNSD); aerosols; aircraft campaign observations; machine learning; explainable artificial intelligence (xAI)
Categories
Funding
- National Aeronautics and Space Administration (NASA) [NNX17AG35G]
- National Science Foundation (NSF) [AGS-1550816]
- NASA [NNX15AJ23G, NNX15AH33A, 80NSSC19K0124, 80NSSC18K0630]
- NSF [AGS-1650786, AGS1652688, AGS-1660486, 006784]
- National Research Foundation of Korea (NRF) - Korean government (MSIT) [NRF-2018R1A2B2006965]
- NSF Atmospheric and Geospace Sciences Postdoctoral Research Fellowship (AGS-PRF) [1524860]
- National Oceanic and Atmospheric Administration (NOAA) [NA17OAR4310012]
- NOAA [NA17OAR4310010]
- NASA [NNX15AJ23G, 807497] Funding Source: Federal RePORTER
- Div Atmospheric & Geospace Sciences
- Directorate For Geosciences [1524860] Funding Source: National Science Foundation
Ask authors/readers for more resources
Cloud condensation nuclei play a vital role in aerosol-cloud interactions and contribute to the largest uncertainty in climate change prediction. A machine learning model is proposed to accurately quantify CCN from simulated data, providing a pathway for global climate models to improve accuracy and confidence in assessment of anthropogenic contributions and climate change projections.
Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model-simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol-cloud interactions (ERFaci) and improving confidence in assessment of anthropogenic contributions and climate change projections.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available