Predictability and Risk of Extreme Winter PM_(2.5)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 PM_(2.5)concentration in Beijing based on the UNprecedented Simulation of Extremes with ENsembles(UNSEEN)method.The PM_(2.5)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 10,000proxy series are generated with the model fidelity test.It is found that in Beijing,the main meteorological driver of PM_(2.5)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 PM_(2.5)concentration in Beijing.We unravel that there is a 3%(2.1%–3.9%)chance of unprecedented low monthly V850 corresponding to high PM_(2.5)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 PM_(2.5)in Beijing in recent years.

Spatiotemporal distribution and decadal change of the monthly temperature predictability limit in China

Based on the nonlinear Lyapunov exponent and nonlinear error growth dynamics, the spatiotemporal distribution and decadal change of the monthly temperature predictability limit(MTPL) in China is quantitatively analyzed. Data used are daily temperature of 518 stations from 1960 to 2011 in China. The results are summarized as follows:(1) The spatial distribution of MTPL varies regionally. MTPL is higher in most areas of Northeast China, southwest Yunnan Province, and the eastern part of Northwest China. MTPL is lower in the middle and lower reaches of the Yangtze River and Huang-huai Basin.(2)The spatial distribution of MTPL varies distinctly with seasons. MTPL is higher in boreal summer than in boreal winter.(3) MTPL has had distinct decadal changes in China, with increase since the 1970 s and decrease since2000. Especially in the northeast part of the country, MTPL has significantly increased since 1986. Decadal change of MTPL in Northwest China, Northeast China and the Huang-huai Basin may have a close relationship with the persistence of temperature anomaly. Since the beginning of the 21 st century, MTPL has decreased slowly in most of the country, except for the south. The research provides a scientific foundation to understand the mechanism of monthly temperature anomalies and an important reference for improvement of monthly temperature prediction.