4.6 Article

Synergistic Effect of El Nino and Arctic Sea-Ice Increment on Wintertime Northeast Asian Anomalous Anticyclone and Its Corresponding PM2.5 Pollution

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2022JD037840

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air pollution; El Nino; Arctic sea ice; synergistic effect; North China Plain

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The October Barents-Kara Seas sea ice and the early winter El Nino have a synergistic effect on the northeast Asian anomalous anticyclone (NAAA), leading to the deterioration of air quality over the North China Plain (NCP). If both El Nino and more October sea ice in the Barents-Kara Seas occur at the same time, there is a 57% probability of a strong NAAA and a 43% probability of PM2.5 pollution over the NCP, which is higher than when only more sea ice or El Nino occurs separately.
PM2.5 pollution frequently occurs over the North China Plain (NCP) during early winter, which is usually modulated by the northeast Asian anomalous anticyclone (NAAA). Research into the NAAA and the related PM2.5 pollution has mainly focused on the individual impact of El Nino or Arctic sea ice, with less analysis on their synergistic effect. This study finds that the October Barents-Kara Seas sea ice and the early winter El Nino synergistically exert distinct remote influence on the NAAA via propagation of Rossby waves and finally deteriorate air quality over the NCP. More specifically, the more Barents-Kara Seas sea ice in October tends to cause a cooling there in November, which stimulates a Rossby wave train and thus strengthens the early winter NAAA. As a result, the weakened ventilation conditions and stagnant air support PM2.5 pollution over the NCP. Further results reveal that if there are El Nino and the more October Barents-Kara Seas sea ice at the same time, the probability of a strong NAAA is 57% and the associated probability of PM2.5 pollution over the NCP is 43%, which are more than that if only the more Barents-Kara Seas sea ice (0%) or El Nino (14%). Considering the key role of the NAAA to PM2.5 pollution over the NCP, we therefore construct a simple linear model that can skillfully predict early winter NAAA at a lead time of 1 month, which shows good performance with correlation coefficient of up to 0.61 in raw NAAA.

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