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Discussion papers
https://doi.org/10.5194/acp-2018-916
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-916
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Sep 2018

Research article | 25 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

A seesaw haze pollution in North China modulated by sub-seasonal variability of atmospheric circulation

Ge Zhang1, Yang Gao1,2, Wenju Cai3,4, L. Ruby Leung5, Shuxiao Wang6, Bin Zhao7, Minghuai Wang8, Huayao Shan1, Xiaohong Yao1,2, and Huiwang Gao1,2 Ge Zhang et al.
  • 1Key Laboratory of Marine Environmental Science and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China
  • 2Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China
  • 3Physical Oceanography Laboratory/CIMST, Ocean University of China and Qingdao, National Laboratory for Marine Science and Technology, Qingdao, 266100, China
  • 4CSIRO Marine and Atmospheric Research, Aspendale, Victoria, 3195, Australia
  • 5Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
  • 6State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
  • 7Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA
  • 8School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China

Abstract. Utilizing a recent observational particulate matter with diameters less than 2.5µm (PM2.5) dataset in North China, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in the adjacent two months of December 2015 and January 2016, accompanied by distinct meteorological modulations. The seesaw pattern is postulated to be linked to super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian Winter Monsoon (EAWM) and the associated low-level southerly wind anomaly reduced planetary boundary layer height, favoring strong haze formation. This circulation pattern was completely reversed in the following month, in part due to a sudden phase change of the AO from positive to negative and the beginning of a decay of the El Niño, which enhanced the southward shift of the upper-tropospheric jet from December to January relative to climatology, leading to an enhanced EAWM and substantially lower haze formation. This sub-seasonal change in circulation is also robustly found in 1982/1983 and 1997/1998, implicative of a general physical mechanism dynamically linked to El Niño and the AO. Numerical experiments with the Weather Research and Forecasting (WRF)-Community Multi-scale Air Quality (CMAQ) model were used to test the modulation of the meteorological conditions on haze formation. With the same emission, simulations for three super El Niño periods (1983, 1997 and 2015) robustly show higher PM2.5 concentrations under the mature phase of the super El Niño, but substantially lower PM2.5 concentrations during the decay phase of El Niño (and the sudden AO phase change), further verifying the modulation effect of sub-seasonal circulation anomaly on PM2.5 concentrations in North China.

Ge Zhang et al.
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Based on observed data, this study reveals a distinct seesaw feature of abnormally high and low PM2.5 concentrations in December 2015 and January 2016 over North China. The mechanism of the seesaw pattern was found to be linked to super El Niño and the Arctic Oscillation (AO). During the mature phase of El Niño in December 2015, the weakened East Asian Winter Monsoon favors strong haze formation, however, the circulation pattern was reversed in the following month due to the phase change of AO.
Based on observed data, this study reveals a distinct seesaw feature of abnormally high and low...
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