Published in npj Climate and Atmospheric Science, the research, led by Yuan Chen and Yongwen Zhang from the Data Science Research Center at Kunming University of Science and Technology, along with collaborators, explored the impact of global SSTAs on the seasonal predictability of China's surface ozone.

The findings shed light on the escalating challenge of surface ozone pollution in China. Despite the Chinese government's efforts to implement air pollution control measures, surface ozone concentrations in eastern China have alarmingly increased. Given the potential health and environmental risks, predicting future surface ozone concentrations becomes crucial. In this context, the research bridges the gap between SSTAs and the seasonal predictability of China's surface ozone patterns.

Employing eigen techniques, the researchers characterized dominant surface ozone patterns over China. They correlated these patterns with global SSTA time series using an advanced autocorrelation-preserving significance test. The results reveal that summer ozone pollution is linked to the Walker circulation, the North Pacific High, the West Pacific Subtropical High, and the Pacific-North American teleconnection pattern corresponding to four SSTA clusters. These atmospheric circulations with anomalies create favorable environments for photochemical reactions that generate surface ozone pollution. Furthermore, winter ozone pollution is associated with the Southern Oscillation, the North Atlantic Oscillation, the Amundsen Sea Low, and the Madden-Julian Oscillation.

To enhance predictive capabilities, the researchers proposed a statistical model to forecast the dominant surface ozone pattern in China for both summer and winter seasons. This model is associated with the states of the identified SSTA clusters, with a lead time of at least 3 months. Using the training dataset from 2013 to 2017, the model demonstrates high prediction accuracy, achieving R-values of 0.89 and 0.81 for summer and winter, respectively. In the testing dataset from 2018 to 2022, the R-values remain close to 0.5 for both seasons, indicating the model's proficiency in capturing general trends in the time series.

 This achievement not only reveals the seasonal patterns of ozone pollution under the influence of crucial SSTA clusters linked to atmospheric circulations but also provides a potential seasonal prediction tool. The study can assist governments and environmental agencies in better developing air quality management measures, thereby mitigating the health and environmental risks posed by ozone pollution in China.