利用1961～2016年京津冀90 个国家级气象站的霾数据集、日平均相对湿度、14:00 能见度、日平均气温、日累计降水、日平均风速、辐射等数据,采用MASH 方法、线性趋势分析方法、曼-肯德尔(Mann-Kendall)法以及相关分析等方法研究了京津冀地区霾天气影响下的气候变化特征.结果表明:京津冀地区年平均霾日数呈明显的增长趋势,增长率为5d/10a 以上,大中城市年平均霾日数明显高于其他地区,霾日数突变增多发生在1992～1993年,在国家大气污染防治专项资金注入以后年平均霾日数增长趋势减缓;京津冀地区霾日和非霾日年平均气温呈上升趋势,年平均能见度呈下降趋势;年平均降水日数总体趋势减少,但是霾日降水日数逐年增加,而非霾日呈现减少趋势,两者呈现对称相反关系;京津冀霾日和非霾日年平均风速均呈逐年下降趋势;霾日数具有随着GDP、能源消耗的增加逐年递增趋势.京津冀地区霾日和非霾日年平均总辐射和散射辐射都是逐年下降,霾日比非霾日下降趋势更加明显,年平均总辐射比散射辐射下降明显;年平均霾日数与年平均总辐射、年平均风速、年平均降水呈负相关,但是与年平均气温、GDP、能源消耗呈正相关.%Based on the daily datasets ofhaze, relative humidity, visibility at 14:00, air temperature, wind speed and radiation from 90 national meteorological stations in the Beijing-Tianjin-Hebei region (BTH) during 1961～2016, the climate change in BTH on haze days was analyzed by using the methods of MASH, linear regression, correlation analysis and Mann-Kendall. The results suggested that: the annual mean haze days in BTH showed an obvious enhanced trend, the increase was above 5d/10a, and the haze days in the middle and large cities were obviously higher compared to other areas. The sharp increase of haze days began in 1991～1992. The annual mean haze trend was slowed down under the initiation of special funds for the air pollution prevention and control. The daily mean temperature of haze and non-haze days showed an upward trend in BTH, whereas annual visibility decreased. The overall trend of annual precipitation decreased in BTH. The annual precipitation days of haze daily increased, while it showed a decreasing trend in non-haze days. The annual mean wind of haze and non-haze days in BTH decreased. The haze day increased with the enhanced GDP and energy consumption. The annual mean total radiation and scattering radiation of haze and non-haze days had been decliningin BTH. The declined trend of haze days is more obvious in contrast with non-haze days, and the annual mean total radiation is decreased remarkable than the scattering radiation. The annual mean haze days were negatively correlated with annual mean values of radiation, wind speed and precipitation, but positively correlated with annual mean variables of temperature, GDP and energy consumption.
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