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https://doi.org/10.5194/egusphere-2025-1483
https://doi.org/10.5194/egusphere-2025-1483
03 Jun 2025
 | 03 Jun 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere

Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang

Abstract. The accurate prediction of cloud condensation nuclei (CCN) number concentration (NCCN) on a large spatiotemporal scale is challenging but critical to evaluate the aerosol cloud interaction (ACI) effect. Combining multi-source dataset and the NCCN simulated by the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model, we have developed a new machine learning-based model which predicts well both regional and hourly-to-yearly scale NCCN at typical supersaturations in the North China Plain (NCP). We show that the prediction bias of NCCN compared to observations is reduced from -39 % with the WRF-Chem model to approximately -8 % with the new model. The greatest improvement is seen in polluted cases. The new model captures well the spatial variation and better describes long-term trends of NCCN than the WRF-Chem. More importantly, the study reveals a significant long-term decreasing trend of NCCN in NCP due to a rapid reduction in aerosol concentrations from 2014 to 2018, during which a series of strict emission reduction measures were implemented by the Chinese government. This reflects the climate benefit of pollution control. Our study further illustrates that the new model reduces the uncertainty in simulating cloud radiative forcing from an overestimation of 1.07±0.76 W m-2 to only 0.18±0.65 W m-2, illustrating the high sensitivity of climate forcing to changes in NCCN. This work offers a new modeling framework that has the potential to greatly improve the assessment of the ACI effect in current models, and guides the way to simulate CCN in other regions around the world.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang

Status: open (until 29 Jul 2025)

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Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang
Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang

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Short summary
In this study, a new framework of cloud condensation nuclei (CCN) prediction in polluted region has been developed and it achieves well prediction of hourly-to-yearly scale across North China Plain. The study reveals a significant long-term decreasing trend of CCN concentration at typical supersaturations due to a rapid reduction in aerosol concentrations from 2014 to 2018. This improvement of our new model would be helpful to aerosols climate effect assessment in models.
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