Articles | Volume 22, issue 17
https://doi.org/10.5194/acp-22-11173-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-11173-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Predicting gridded winter PM2.5 concentration in the east of China
Zhicong Yin
CORRESPONDING AUTHOR
Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change
(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing, 210044, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, 519080, China
Nansen-Zhu International Research Centre, Institute of Atmospheric
Physics, Chinese Academy of Sciences, Beijing, 100029, China
Mingkeng Duan
Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change
(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing, 210044, China
Yuyan Li
Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change
(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing, 210044, China
Tianbao Xu
Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change
(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing, 210044, China
Huijun Wang
Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change
(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing, 210044, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, 519080, China
Nansen-Zhu International Research Centre, Institute of Atmospheric
Physics, Chinese Academy of Sciences, Beijing, 100029, China
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Winter extreme rainfall causes severe floods. Its link to the stratospheric polar vortex was thought weak. We found this hidden link is unlocked by a tropical oscillation. When the vortex shifts toward Eurasia, it amplifies rainfall only during certain phases. Two routes act: the vortex lifts warm moist air and strengthens tropical moisture supply. Climate models show capturing this tropical pathway is key. Joint monitoring of the vortex and oscillation may improve predictions.
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Dust days during the spring seasons of 2015–2023 in North China were classified into Mongolian cyclone and cold high types depending on the presence of the Mongolian cyclone. The Mongolian cyclone type led to more frequent and severe dust weather, indicated by PM10 concentrations. To comprehensively forecast the two types of dust weather, a common predictor was established based on 500 hPa anomalous circulation systems, offering insights for dust weather forecasting and climate prediction.
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This study classified the winter cold surge in eastern China into blocking cold surge and wave-train cold surge and investigated the difference of haze dispersion ability between the two types. The results show that the haze dispersion of blocking cold surge is weaker than that of wave-train cold surge. In the past 4 decades, the frequency of wave-train (blocking) cold surge shows a downward (upward) trend, which means that the ability of cold surge to disperse haze is declining.
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Short summary
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Winter extreme rainfall causes severe floods. Its link to the stratospheric polar vortex was thought weak. We found this hidden link is unlocked by a tropical oscillation. When the vortex shifts toward Eurasia, it amplifies rainfall only during certain phases. Two routes act: the vortex lifts warm moist air and strengthens tropical moisture supply. Climate models show capturing this tropical pathway is key. Joint monitoring of the vortex and oscillation may improve predictions.
Qianyi Huo, Zhicong Yin, Xiaoqing Ma, and Huijun Wang
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Dust days during the spring seasons of 2015–2023 in North China were classified into Mongolian cyclone and cold high types depending on the presence of the Mongolian cyclone. The Mongolian cyclone type led to more frequent and severe dust weather, indicated by PM10 concentrations. To comprehensively forecast the two types of dust weather, a common predictor was established based on 500 hPa anomalous circulation systems, offering insights for dust weather forecasting and climate prediction.
Xiaoqing Ma and Zhicong Yin
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Severe ozone pollution frequently occurred in the east of China and obviously damages human health. The meteorological conditions effectively affect the variations in ozone pollution by modulating the natural emissions of ozone precursors and photochemical reactions in the atmosphere. In this study, a south–north dipole pattern of summer-mean ozone concentration in the east of China was identified, and its connections with preceding climate variability at different latitudes were also examined.
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Short summary
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This study classified the winter cold surge in eastern China into blocking cold surge and wave-train cold surge and investigated the difference of haze dispersion ability between the two types. The results show that the haze dispersion of blocking cold surge is weaker than that of wave-train cold surge. In the past 4 decades, the frequency of wave-train (blocking) cold surge shows a downward (upward) trend, which means that the ability of cold surge to disperse haze is declining.
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Short summary
The PM2.5 concentration has been greatly reduced in recent years in China and has entered a crucial stage that required fine seasonal prediction. However, there is still no study aimed at predicting gridded PM2.5 concentration. A model for seasonal prediction of gridded winter PM2.5 concentration in the east of China was developed by analyzing the contributions of emissions and climate variability, which could provide scientific support for air pollution control at the regional and city levels.
The PM2.5 concentration has been greatly reduced in recent years in China and has entered a...
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