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.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
61
References
24
Citations
NaN
KQI