该文主要介绍了科技部国家重点基础研究发展规划项目“首都北京及周边地区大气、水、土环境污染机理及调控原理”大气分项目的研究成果。项目分别于2001年和2003年重点开展了BECAPEX科学试验(Beijing City AirPollution Experiment)。BE...该文主要介绍了科技部国家重点基础研究发展规划项目“首都北京及周边地区大气、水、土环境污染机理及调控原理”大气分项目的研究成果。项目分别于2001年和2003年重点开展了BECAPEX科学试验(Beijing City AirPollution Experiment)。BECAPEX试验同步进行城市边界层气象与大气化学观测,通过卫星遥感、地面观测,即城市空间和地面以及点与面结合的技术途径,以揭示北京城市污染“空气穹隆”大气化学结构特征及其变化规律,为城市环境大气动力-化学模式提供基本科学参数,给出城市边界层大气物理化学过程综合模型,为提高城市环境大气物理-化学过程耦合模式的准确性和可靠性提供科学依据。该项目揭示了北京城区和城近郊区城市边界层结构与湍流特征,城市大气污染垂直结构特征;发现了城市大气污染空间结构多尺度特征,其中包括大气污染源影响和城市热岛多尺度特征;揭示了城市大气重污染过程周边源影响域,以及北京及周边地区气溶胶影响域和区域气候响应;提出了北京市典型污染源排放清单;发展了城市气象模式系统,包括冠层模式、街谷环流和热力结构以及城市高大建筑群周围风环境数值模拟;发展了空气质量模式技术,包括二次气溶胶模拟试验、北京地区SO2污染的长期模拟及不同类型排放源影响的计算与评估、影响北京地区的沙尘暴输送模拟、区域化学输送模式中NOx和O3源示踪法,城市尺度的大气污染CAPPS模式及统计模型的应用、大气污染及紫外辐射数值预报模式和CMAQ-MOS空气质量预报方法;改进了美国公共多尺度空气质量预报模式,建立了CMAQ-MOS区域空气质量动力-统计模型预报模式,以及发展的源同化技术,突破了当前空气质量模式技术“瓶颈”,使模式预报准确率明显提高。展开更多
The boreal spring Antarctic Oscillation(AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP's Climate Forecast System, version 2(CFSv2), in pred...The boreal spring Antarctic Oscillation(AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP's Climate Forecast System, version 2(CFSv2), in predicting the boreal spring AAO for the period 1983–2015. The results indicate that CFSv2 has poor skill in predicting the spring AAO, failing to predict the zonally symmetric spatial pattern of the AAO, with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI). Considering the interannual increment approach can amplify the prediction signals, we firstly establish a dynamical–statistical model to improve the interannual increment of the AAOI(DY AAOI), with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice. This dynamical–statistical model demonstrates good capability in predicting DY AAOI, with a significant correlation coefficient of 0.58 between the observation and prediction during 1983–2015 in the two-year-out cross-validation. Then, we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI. The improved AAOI shows a significant correlation coefficient of 0.45 with the observed AAOI during 1983–2015. Moreover, the unrealistic atmospheric response to March–April–May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO. This study gives new clues regarding AAO prediction and short-term climate prediction.展开更多
文摘该文主要介绍了科技部国家重点基础研究发展规划项目“首都北京及周边地区大气、水、土环境污染机理及调控原理”大气分项目的研究成果。项目分别于2001年和2003年重点开展了BECAPEX科学试验(Beijing City AirPollution Experiment)。BECAPEX试验同步进行城市边界层气象与大气化学观测,通过卫星遥感、地面观测,即城市空间和地面以及点与面结合的技术途径,以揭示北京城市污染“空气穹隆”大气化学结构特征及其变化规律,为城市环境大气动力-化学模式提供基本科学参数,给出城市边界层大气物理化学过程综合模型,为提高城市环境大气物理-化学过程耦合模式的准确性和可靠性提供科学依据。该项目揭示了北京城区和城近郊区城市边界层结构与湍流特征,城市大气污染垂直结构特征;发现了城市大气污染空间结构多尺度特征,其中包括大气污染源影响和城市热岛多尺度特征;揭示了城市大气重污染过程周边源影响域,以及北京及周边地区气溶胶影响域和区域气候响应;提出了北京市典型污染源排放清单;发展了城市气象模式系统,包括冠层模式、街谷环流和热力结构以及城市高大建筑群周围风环境数值模拟;发展了空气质量模式技术,包括二次气溶胶模拟试验、北京地区SO2污染的长期模拟及不同类型排放源影响的计算与评估、影响北京地区的沙尘暴输送模拟、区域化学输送模式中NOx和O3源示踪法,城市尺度的大气污染CAPPS模式及统计模型的应用、大气污染及紫外辐射数值预报模式和CMAQ-MOS空气质量预报方法;改进了美国公共多尺度空气质量预报模式,建立了CMAQ-MOS区域空气质量动力-统计模型预报模式,以及发展的源同化技术,突破了当前空气质量模式技术“瓶颈”,使模式预报准确率明显提高。
基金supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600703)the funding of the Jiangsu Innovation & Entrepreneurship Team and the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The boreal spring Antarctic Oscillation(AAO) has a significant impact on the spring and summer climate in China. This study evaluates the capability of the NCEP's Climate Forecast System, version 2(CFSv2), in predicting the boreal spring AAO for the period 1983–2015. The results indicate that CFSv2 has poor skill in predicting the spring AAO, failing to predict the zonally symmetric spatial pattern of the AAO, with an insignificant correlation of 0.02 between the predicted and observed AAO Index(AAOI). Considering the interannual increment approach can amplify the prediction signals, we firstly establish a dynamical–statistical model to improve the interannual increment of the AAOI(DY AAOI), with two predictors of CFSv2-forecasted concurrent spring sea surface temperatures and observed preceding autumn sea ice. This dynamical–statistical model demonstrates good capability in predicting DY AAOI, with a significant correlation coefficient of 0.58 between the observation and prediction during 1983–2015 in the two-year-out cross-validation. Then, we obtain an improved AAOI by adding the improved DY AAOI to the preceding observed AAOI. The improved AAOI shows a significant correlation coefficient of 0.45 with the observed AAOI during 1983–2015. Moreover, the unrealistic atmospheric response to March–April–May sea ice in CFSv2 may be the possible cause for the failure of CFSv2 to predict the AAO. This study gives new clues regarding AAO prediction and short-term climate prediction.
