Climate modulation of Nino3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution

被引:19
作者
Cheng, Xugeng [1 ,5 ]
Boiyo, Richard [2 ]
Zhao, Tianliang [1 ]
Xu, Xiangde [3 ]
Gong, Sunling [4 ]
Xie, Xiaoning [5 ]
Shang, Ke [5 ]
机构
[1] Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys,Key Lab Meteorol Disaster,Coll, Key Lab Aerosol Cloud Precipitat,Minist Educ KLME, China Meteorol Adm,Int Joint Lab Climate & Enviro, Nanjing 210044, Jiangsu, Peoples R China
[2] Meru Univ Sci & Technol, Dept Phys Sci, POB 972-60200, Meru, Kenya
[3] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China
[4] Chinese Acad Meteorol Sci, Inst Atmospher Composit, Key Lab Atmospher Chem, CMA, Beijing 100081, Peoples R China
[5] Chinese Acad Sci, Inst Earth Environm, SKLLQG, Xian 710061, Shaanxi, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Air quality; Haze; Southern China; Random forest; SSTA; SEA-SURFACE TEMPERATURE; EAST ASIAN TELECONNECTION; WINTER HAZE; EL-NINO; WEATHER CONDITIONS; AEROSOL VARIATIONS; UNITED-STATES; RIVER DELTA; MONSOON; VARIABILITY;
D O I
10.1016/j.atmosres.2019.04.002
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
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 Nino3.4 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 Nino3.4 region. The winters with warm Nino3.4 SSTAs in (El Nino) 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 Nino3.4 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 Nino3.4 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.
引用
收藏
页码:157 / 164
页数:8
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