Seesaw haze pollution in North China modulated by the sub-seasonal variability of atmospheric circulation

2018 
Abstract. Utilizing a recent observational dataset of particulate matter with diameters less than 2.5 µ m (PM 2.5 ) in North China, this study reveals a distinct seesaw feature of abnormally high and low PM 2.5 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 a super El Nino and the Arctic Oscillation (AO). During the mature phase of El Nino in December 2015, the weakened East Asian winter monsoon (EAWM) and the associated low-level southerly wind anomaly reduced planetary boundary layer (PBL) 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 Nino, 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 Nino and the AO. Numerical experiments using the Weather Research and Forecasting (WRF) Community Multiscale 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 Nino periods (1983, 1997 and 2015) robustly show higher PM 2.5  concentrations under the mature phase of the super El Nino, but substantially lower PM 2.5 concentrations during the decay phase of El Nino (and the sudden AO phase change), further verifying the modulation effect of the sub-seasonal circulation anomaly on PM 2.5 concentrations in North China.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    61
    References
    24
    Citations
    NaN
    KQI
    []