Seasonal predictability of the dominant surface ozone pattern over China linked to sea surface temperature

Yuan Chen, Dean Chen, Linru Nie, Wenqi Liu, Jingfang Fan, Xiaosong Chen, Yongwen Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Mitigation surface ozone pollution becomes increasingly pivotal in improving China’s air quality. However, the impact of global sea surface temperature anomalies (SSTA) on the long-term predictability of China’s surface ozone remains challenging. In this study, we employ eigen techniques to effectively characterize dominant surface ozone patterns over China, and establish cross-correlations between the dominant patterns and global SSTA time series. Our findings reveal that China’s summer ozone pollution is strongly associated with crucial SSTA clusters linked to atmospheric circulations, i.e., the West Pacific Subtropical High and the Pacific-North American teleconnection pattern. For winter, ozone pollution is attributed to SSTA clusters related to the Southern Oscillation, the Madden-Julian Oscillation and others. We propose a multivariate regression model capable of predicting surface ozone patterns with a lead time of at least 3 months. Evaluation of our model using a testing dataset yields an R-value of around 0.5 between predicted and observed data, surpassing statistical significance threshold. This suggests the viability and potential applicability of our predictive model in surface ozone forecasting and mitigation strategies in China.

Original languageEnglish
Article number17
Journalnpj Climate and Atmospheric Science
Volume7
Issue number1
DOIs
StatePublished - Dec 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Funding

The authors thank the financial support by the National Key Research and Development Program of China (Grant No. 2023YFE0109000), the National Natural Science Foundation of China (Grant No. 12305044, 12371460 and 12135003) and the Fundamental Research Program of Yunnan Province (No. CB22052C173A). We also thank the data source provided by Tsinghua University’s Tracking Air Pollution team ( https://quotsoft.net/air/ ), European Centre for Medium-Range Weather Forecasts ( https://cds.climate.copernicus.eu/ ) and website ( http://tapdata.org.cn/ ). The authors thank the financial support by the National Key Research and Development Program of China (Grant No. 2023YFE0109000), the National Natural Science Foundation of China (Grant No. 12305044, 12371460 and 12135003) and the Fundamental Research Program of Yunnan Province (No. CB22052C173A). We also thank the data source provided by Tsinghua University’s Tracking Air Pollution team (https://quotsoft.net/air/), European Centre for Medium-Range Weather Forecasts (https://cds.climate.copernicus.eu/) and website (http://tapdata.org.cn/).

FundersFunder number
Fundamental Research Program of Yunnan ProvinceCB22052C173A
Tsinghua University’s Tracking Air Pollution
National Natural Science Foundation of China12371460, 12135003, 12305044
National Key Research and Development Program of China2023YFE0109000
European Centre for Medium-Range Weather Forecasts

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