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

Abstract

China has witnessed notable increases in surface ozone (O3) concentrations since 2013, with meteorology identified as a critical driver. However, meteorological contributions vary with different meteorological datasets and analytical methods, and their uncertainties remain unassessed. This study leveraged decadal observational maximum daily 8-hour average O3 records (2013-2022) across China, revealing intensified nationwide O3 pollution with increasing O3 trends of 0.79-1.31 ppb yr-1 during four seasons. We gave special focus on uncertainties of meteorology-driven O3 trends by using diverse meteorological datasets (ERA5, MERRA2, FNL) and diverse analytical methods (Multiple Linear Regression, Random Forest, GEOS-Chem model). A useful statistic (coefficient of variation, CV) was adopted as an uncertainty quantification metric. For multi-dataset analysis, models driven by different meteorological datasets exhibited the maximum meteorology-driven O3 trend (+0.55 ppb yr-1, multi-dataset mean) with the highest consistency (CV Combining double low line 0.25) in spring. The FNL-driven model always obtained larger trends compared to ERA5 and MERRA2, which could be attributed to inability to accurately evaluate planetary boundary layer height in FNL dataset. For multi-method analysis, three methods demonstrated optimal consistency in winter (CV Combining double low line 0.40) and the worst consistency in summer (CV Combining double low line 2.00). The meteorology-driven O3 trends obtained from GEOS-Chem model were almost smaller than those obtained by other two methods, partly resulting from higher simulated O3 values before 2018. Overall, all analyses driven by diverse meteorological datasets and analytical methods drew a robust conclusion that meteorological conditions almost boosted O3 increases during all seasons; the uncertainties caused by different analytical methods were larger than those caused by diverse meteorological datasets.