Abstract

Understanding the spatial–temporal evolution of China haze pollution (HP) and identifying its precursory signals is of central importance. Based on observational data across central-eastern China (CEC) for the period of 1980–2015, this study finds that HP interannual variations in the CEC region during the most polluted season (December–January) are mainly dominated by a homogeneous pattern with maximum loading centers located over the Central China, Yangtze River Delta, and South China, which accounts for 42.3% of the total variances. The circulations conducive to such polluted mode feature barotropic high anomalies over central-eastern China. Correspondently, the related anomalous weather and circulation conditions with less precipitation, low-level dryer air, weaker surface wind speed, more descending movement, and more upstream transportation of aerosols at the northeast and southeast planks of the anomalous high, leading to the HP frequently occur in the CEC region, and vice versa. Observed evidences show that the combined effects of the Southern Hemisphere tropospheric and stratospheric polar vortices (SHPV) in August and September (AS), can exert profound effects on the HP dominant mode. Notable circulation anomalies associated with the combined effects of the SHPV, prevailing over the ocean off the south-west Western Australia, can induce sea surface temperature (SST) anomalies around the ocean off the south-west Western Australia. This anomalous SST pattern in AS can persist into the following early winter and impact the HP variations by modulating the Hadley cell and correspondently circulation anomalies over central-eastern China. Based on the preseason SHPV signal, we establish a physical-empirical seasonal prediction model to predict the HP dominant mode variations. The hindcast shows a promising prediction skill. Since the regional PV anomalies linked to the combined effects of the SHPV could be monitored in preceding autumn, the HP over the CEC in early winter can be forecasted in real-time by this empirical model.

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