Intra-seasonal variability of weather patterns is of great significance for the occurrence and evolution of air pollution and the forecast of future air quality.

Previous studies have focused on haze episodes from the perspective of weather processes and local meteorological conditions, but little is known about the influence of intra-seasonal variability of weather circulation on the occurrence and evolution of air pollution.

Recently, researchers from the Institute of Earth Environment (IEE) of the Chinese Academy of Sciences and their collaborators examined the impact of the intra-seasonal variability of mid-tropospheric westerly circulation and surface Siberian High on air quality during the 2015-2016 winter. They used ECMWF ERA-interim reanalysis data, ambient PM_2.5 mass concentrations monitored by the Chinese Ministry of Ecology and Environment and a coupled online WRF-CHEM model.

Researchers found that a wavy westerly circulation at 500 hPa corresponded to a strengthened surface Siberian High, causing much southward cold fresh air advection and low PM_2.5 concentrations in eastern China. While a straight westerly circulation prevailed at 500 hPa, there was a weaker surface Siberian High, confining cold air within high latitudes, isolating warm air at the mid-latitudes, and causing higher PM_2.5 concentrations in eastern China.

Difference in the westerly circulation significantly induced change in PM_2.5 concentrations over eastern China with the maximal change exceeding 200μg·m^-3 by simulating two extreme weather patterns.

This study, published in Journal of Geophysical Research: Atmospheres, investigated how intra-seasonal variability of weather patterns affect wintertime air pollution in China.

These findings illustrate that understanding the intra-seasonal variability of weather patterns mechanically is important to improve short-term air quality forecast and warning. In the future, short-term climate prediction could be applied to air pollution mitigation and prevention in operational forecast.

Spatial distributions of surface PM_2.5 concentrations on the heavily polluted days (a) and clean days (b) under two extremely different weather patterns. (c) and (d) for winds (the arrows, m·s^-1) and temperature (the shading with borders, °C) at 500 hPa. (e) and (f) for winds (the arrows, m·s^-1) and sea level pressure (the shading with borders, hPa) at the surface. (Image by ZHAO Shuyu et. al)