Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021GL094133 |
Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles | |
Arshad Arjunan Nair; Fangqun Yu; Pedro Campuzano-Jost; Paul J. DeMott; Ezra J. T. Levin; Jose L. Jimenez; Jeff Peischl; Ilana B. Pollack; Carley D. Fredrickson; Andreas J. Beyersdorf; Benjamin A. Nault; Minsu Park; Seong Soo Yum; Brett B. Palm; Lu Xu; Ilann Bourgeois; Bruce E. Anderson; Athanasios Nenes; Luke D. Ziemba; Richard H. Moore; Taehyoung Lee; Taehyun Park; Chelsea R. Thompson; Frank Flocke; Lewis Gregory Huey; Michelle J. Kim; Qiaoyun Peng | |
2021-10-16 | |
发表期刊 | Geophysical Research Letters |
出版年 | 2021 |
英文摘要 | 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/artificial intelligence 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 machine learning model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. Machine learning extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust machine learning 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. |
领域 | 气候变化 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/340064 |
专题 | 气候变化 |
推荐引用方式 GB/T 7714 | Arshad Arjunan Nair,Fangqun Yu,Pedro Campuzano-Jost,et al. Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles[J]. Geophysical Research Letters,2021. |
APA | Arshad Arjunan Nair.,Fangqun Yu.,Pedro Campuzano-Jost.,Paul J. DeMott.,Ezra J. T. Levin.,...&Qiaoyun Peng.(2021).Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles.Geophysical Research Letters. |
MLA | Arshad Arjunan Nair,et al."Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles".Geophysical Research Letters (2021). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论