Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, China
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, China
Li Qi
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, China
Qiaohua Zhao
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, China
Jinhai He
Key Laboratory of Meteorological Disaster, Ministry of Education
(KLME), Joint International Research Laboratory of Climate and Environment
Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science and Technology, Nanjing, China
Julian X. L. Wang
Air Resources Laboratory, National Oceanic and Atmospheric
Administration, College Park, MD, USA
Viewed
Total article views: 3,940 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,924
907
109
3,940
447
115
148
HTML: 2,924
PDF: 907
XML: 109
Total: 3,940
Supplement: 447
BibTeX: 115
EndNote: 148
Views and downloads (calculated since 06 Nov 2018)
Cumulative views and downloads
(calculated since 06 Nov 2018)
Total article views: 3,054 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,288
663
103
3,054
257
110
137
HTML: 2,288
PDF: 663
XML: 103
Total: 3,054
Supplement: 257
BibTeX: 110
EndNote: 137
Views and downloads (calculated since 06 Feb 2019)
Cumulative views and downloads
(calculated since 06 Feb 2019)
Total article views: 886 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
636
244
6
886
190
5
11
HTML: 636
PDF: 244
XML: 6
Total: 886
Supplement: 190
BibTeX: 5
EndNote: 11
Views and downloads (calculated since 06 Nov 2018)
Cumulative views and downloads
(calculated since 06 Nov 2018)
Viewed (geographical distribution)
Total article views: 3,940 (including HTML, PDF, and XML)
Thereof 3,784 with geography defined
and 156 with unknown origin.
Total article views: 3,054 (including HTML, PDF, and XML)
Thereof 2,983 with geography defined
and 71 with unknown origin.
Total article views: 886 (including HTML, PDF, and XML)
Thereof 801 with geography defined
and 85 with unknown origin.
Less attention has been paid to haze weather during the autumn season. Here, we unravel the mechanism of how SST anomalies over the subtropical North Atlantic and western North Pacific drive the interannual variability of the autumnal haze days in the Beijing–Tianjin–Hebei region. The two pathways of SST anomaly forcings can result in an anticyclonic (cyclonic) anomaly over Northeast Asia, leading to a lower-level southerly (northerly) anomaly and in turn more (fewer) haze days in this region.
Less attention has been paid to haze weather during the autumn season. Here, we unravel the...