利用地面细颗粒物(PM2.5)浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学-天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7~11日的一次重霾事...利用地面细颗粒物(PM2.5)浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学-天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7~11日的一次重霾事件及其天气形势、边界层气象特征、输送路径、PM2.5及其化学成分浓度变化等特征,以及秸秆燃烧对华北和北京地区细颗粒物浓度和地面短波辐射的影响。与观测资料的对比结果显示,模式可以很好地模拟北京地区地面气象要素和PM2.5质量浓度,考虑秸秆燃烧排放源可以明显改进北京PM2.5浓度模拟的准确性,但在重度污染情况下,模式总体上低估气溶胶光学厚度和高估地面短波辐射。10月7~11日北京地区重霾事件主要是不利气象条件下人为污染物累积和区域输送造成,也受到华北地区南部秸秆燃烧的影响。河南北部、河北南部和山东西部大面积秸秆燃烧释放的气态污染物和颗粒物在南风的作用下输送至北京,秸秆燃烧对北京地区地面PM2.5、有机碳(OC)、硝酸盐、铵盐、硫酸盐和黑碳(BC)的平均贡献率分别为24.6%、36.8%、23.2%、22.6%、7.1%和19.8%,秸秆燃烧产生的气溶胶可以导致北京地面平均短波辐射最大减小超过20 W m^-2,约占总气溶胶导致地表短波辐射变化的24%。展开更多
An adiabatic bin-sized cloud parcel model is developed by incorporating the multi-chemical-component (MCC) aerosol effects into the UWyo single-chemical-component (SCC) parcel model. The effects of MCC aerosols on the...An adiabatic bin-sized cloud parcel model is developed by incorporating the multi-chemical-component (MCC) aerosol effects into the UWyo single-chemical-component (SCC) parcel model. The effects of MCC aerosols on the warm cloud microphysics in North China are investigated with the model. The simulations are initialized using the data on chemical components and number size distribution of aerosols measured during the IPAC (Influence of Pollution on Aerosols and Cloud Microphysics in North China) campaign in spring 2006. It is found that the MCC aerosols in North China increase the cloud droplet number concentration (CDNC) and decrease the effective radius more efficiently than pure ammonium-sulfate aerosols. It is also shown that the MCC aerosols in North China can narrow the cloud droplet spectra (CDS) by increasing CDNC in small size and decreasing CDNC in large size. Our results indicate that aerosol chemical components and their size distributions can influence the microphysics of warm clouds, and thus affect atmospheric radiation and precipitation. This should attract more attentions in weather and climate change research in the future.展开更多
文摘利用地面细颗粒物(PM2.5)浓度和气象常规观测资料、地基 AERONET观测资料、GFED生物质燃烧排放清单和大气化学-天气耦合模式WRF-Chem,模拟研究了华北地区2014年10月气象要素和大气污染物的时空演变,重点关注北京10月7~11日的一次重霾事件及其天气形势、边界层气象特征、输送路径、PM2.5及其化学成分浓度变化等特征,以及秸秆燃烧对华北和北京地区细颗粒物浓度和地面短波辐射的影响。与观测资料的对比结果显示,模式可以很好地模拟北京地区地面气象要素和PM2.5质量浓度,考虑秸秆燃烧排放源可以明显改进北京PM2.5浓度模拟的准确性,但在重度污染情况下,模式总体上低估气溶胶光学厚度和高估地面短波辐射。10月7~11日北京地区重霾事件主要是不利气象条件下人为污染物累积和区域输送造成,也受到华北地区南部秸秆燃烧的影响。河南北部、河北南部和山东西部大面积秸秆燃烧释放的气态污染物和颗粒物在南风的作用下输送至北京,秸秆燃烧对北京地区地面PM2.5、有机碳(OC)、硝酸盐、铵盐、硫酸盐和黑碳(BC)的平均贡献率分别为24.6%、36.8%、23.2%、22.6%、7.1%和19.8%,秸秆燃烧产生的气溶胶可以导致北京地面平均短波辐射最大减小超过20 W m^-2,约占总气溶胶导致地表短波辐射变化的24%。
基金supported by National Natural Science Foundation of China (Grant No. 40433008)Research and Development Special Fund for Public Welfare Industry (Meteorology) of China Meteorological Administration (Grant Nos. GYHY(QX)-2007-6-36, GYHY(QX)-2007-6-5)Foundation of Nanjing University of Information Science & Technology (Grant No. NUIST-20090218#)
文摘An adiabatic bin-sized cloud parcel model is developed by incorporating the multi-chemical-component (MCC) aerosol effects into the UWyo single-chemical-component (SCC) parcel model. The effects of MCC aerosols on the warm cloud microphysics in North China are investigated with the model. The simulations are initialized using the data on chemical components and number size distribution of aerosols measured during the IPAC (Influence of Pollution on Aerosols and Cloud Microphysics in North China) campaign in spring 2006. It is found that the MCC aerosols in North China increase the cloud droplet number concentration (CDNC) and decrease the effective radius more efficiently than pure ammonium-sulfate aerosols. It is also shown that the MCC aerosols in North China can narrow the cloud droplet spectra (CDS) by increasing CDNC in small size and decreasing CDNC in large size. Our results indicate that aerosol chemical components and their size distributions can influence the microphysics of warm clouds, and thus affect atmospheric radiation and precipitation. This should attract more attentions in weather and climate change research in the future.
