Predictability and Risk of Extreme Winter PM2.5 Concentration in Beijing

Air pollution remains a serious environmental and social problem in many big cities in the world. How to predict and estimate the risk of extreme air pollution is unsettled yet. This study tries to provide a solution to this challenge by examining the winter PM2.5 concentration in Beijing based on the UNprecedented Simulation of Extremes with ENsembles (UNSEEN) method. The PM2.5 concentration observations in Beijing, Japanese 55-yr reanalysis data, and the Met Office near term climate prediction system (DePreSys3a) large ensemble simulations are used, and 10,000 proxy series are generated with the model fidelity test. It is found that in Beijing, the main meteorological driver of PM2.5 concentration is monthly 850-hPa meridional wind (V850). Although the skill in prediction of V850 is low on seasonal and longer timescales, based on the UNSEEN, we use large ensemble of initialized climate simulations of V850 to estimate the current chance and risk of unprecedented PM2.5 concentration in Beijing. We unravel that there is a 3% (2.1%-3.9%) chance of unprecedented low monthly V850 corresponding to high PM2.5 in each winter, within the 95% range, calculated by bootstrap resampling of the data. Moreover, we use the relationship between air quality and winds to remove the meridional wind influence from the observed record, and find that anthropogenic intervention appears to have reduced the risk of extreme PM2.5 in Beijing in recent years.