Meteorological influence on surface ozone trends in China: assessing uncertainties caused by multi-dataset and multi-method

Wang, Xueqing; Zhu, Jia; Jiao, Guanjie; Chen, Xi; Yang, Zhenjiang; Chen, Lei; Jin, Xipeng; Liao, Hong

doi:https://doi.org/10.5194/acp-25-13863-2025

Articles | Volume 25, issue 20

https://doi.org/10.5194/acp-25-13863-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-25-13863-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 20

Research article

|

27 Oct 2025

Research article |

| 27 Oct 2025

Meteorological influence on surface ozone trends in China: assessing uncertainties caused by multi-dataset and multi-method

Xueqing Wang, Jia Zhu, Guanjie Jiao, Xi Chen, Zhenjiang Yang, Lei Chen, Xipeng Jin, and Hong Liao

Supplement

https://doi.org/10.5194/acp-25-13863-2025-supplement

Data sets

Meteorological influence on surface ozone trends in China: Assessing uncertainties caused by multi-dataset and multi-method Xueqing Wang and Jia Zhu https://doi.org/10.5281/zenodo.16916245

geoschem/GCClassic: GEOS-Chem 13.3.3 The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.5748260

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Short summary

Impacts of meteorology on ozone vary with diverse meteorological datasets and analytical methods. Uncertainties of meteorology-driven ozone trends in China were examined. Multi-dataset analysis shows the largest meteorology-driven ozone trend with the best consistency occurs in spring. Multi-method analysis shows the best (worst) consistency occurs in winter (summer). Overall, meteorology boosts ozone growth in all seasons, with uncertainty from multi-method larger than that from multi-dataset.