气候变化领域集成服务门户(CCPortal): Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution

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DOI	10.1016/j.atmosres.2019.04.002
	Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution
	Cheng X.; Boiyo R.; Zhao T.; Xu X.; Gong S.; Xie X.; Shang K.
发表日期	2019
ISSN	0169-8095
起始页码	157
结束页码	164
卷号	225
英文摘要	Air stagnation modulates the frequency and duration of haze events. Based on meteorological and environmental observation data during 1980 to 2013, the present study analyzed the relationship between the interannual variations of Sea Surface Temperature (SST) over tropical central-eastern Pacific and the number of winter haze days (WHD) over the Southern China (SC) region. The potential preceding signal of Sea Surface Temperature anomalies (SSTAs) in Niño3.4 region associated with ENSO can be used as a predictor of haze occurrences in winter. Results indicate that the detrended WHD in the SC is significantly correlated (r = −0.55) with the contemporary SSTAs in Niño3.4 region. The winters with warm Niño3.4 SSTAs in (El Niño) tend to be accompanied with less haze events in the SC, resulting from more local precipitation and enhanced mid-level winds, which helps to build an unstable condition that is conductive for the decrease of haze occurrence. The precursory signal of WHD variability can be detected in the tropical central-eastern Pacific SST, which is amplified since August to winter season. Based on the August–October mean SSTAs in Niño3.4 and three other identified predictors, we developed a seasonal prediction model of the WHD using RF regression method. The model accounted for 90% of the total variance of the WHD in the SC and ranked the SSTAs in Niño3.4 as the most important predictor. This implies that the SSTAs tropical Pacific play a significant role in the variability of WHD in the SC. Since the predictors can be readily monitored in real time, the model provides a real time forecast tool and could brighten the prospects for seasonal forecast of haze anomalies in vulnerable regions such as the SC. © 2019
英文关键词	Air quality; Haze; Random forest; Southern China; SSTA
语种	英语
scopus关键词	Air quality; Atmospheric temperature; Climate change; Decision trees; Forecasting; Nickel compounds; Regression analysis; Submarine geophysics; Surface properties; Surface waters; Tropics; Environmental observation; Haze; Interannual variation; Random forests; Sea surface temperature (SST); Sea surface temperature anomalies; Southern China; SSTA; Oceanography; air quality; air-sea interaction; annual variation; atmospheric pollution; forecasting method; haze; sea surface temperature; temperature anomaly; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162036
作者单位	Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), International Joint Laboratory on Climate and Environment Change (ILCEC), Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China; Department of Physical Sciences, Meru University of Science and Technology, P.O. Box 972-60200, Meru, Kenya; State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China; Institute of Atmospheric Composition/Key Laboratory of Atmospheric Chemistry of China Meteorological Administration (CMA), Chinese Academy of Meteorological Sciences, Beijing, 100081, China; SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China
推荐引用方式 GB/T 7714	Cheng X.,Boiyo R.,Zhao T.,et al. Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution[J],2019,225.
APA	Cheng X..,Boiyo R..,Zhao T..,Xu X..,Gong S..,...&Shang K..(2019).Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution.Atmospheric Research,225.
MLA	Cheng X.,et al."Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution".Atmospheric Research 225(2019).