Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model
Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model
复制标题
利用全球大气化学模型预测极端臭氧污染事件的技能
DOI：
10.5194/acp-14-7721-2014
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发表时间：
2014
影响因子：
6.3
通讯作者：
M. Prather
中科院分区：
文献类型：
--
作者：
J. Schnell;C. Holmes;A. Jangam;M. Prather
抽象的。从监测美国和欧洲地面空气质量的站点集合中，我们确定了极端臭氧污染事件，并发现它们主要发生在空间范围超过1000公里的群集性、多日性事件中。这样的尺度可以用当前的全球大气化学模型进行预测。我们开发了一种客观测绘算法，该算法使用单个地表站点的不同观测数据来计算1°×1°网格单元的平均地表臭氧，与全球模式的分辨率相匹配。空气质量极端(AQX)事件在当地被确定为臭氧气候学的统计极端，而不是空气质量超标。通过加州大学欧文分校的化学传输模型(UCI CTM)，我们发现有技能来事后预测这些极端事件，从而确定使用全球化学气候模型(CCM)来确定气候变暖中极端污染事件特征变化的新诊断方法。
Abstract. From the ensemble of stations that monitor surface air quality over the United States and Europe, we identify extreme ozone pollution events and find that they occur predominantly in clustered, multiday episodes with spatial extents of more than 1000 km. Such scales are amenable to forecasting with current global atmospheric chemistry models. We develop an objective mapping algorithm that uses the heterogeneous observations of the individual surface sites to calculate surface ozone averaged over 1° by 1° grid cells, matching the resolution of a global model. Air quality extreme (AQX) events are identified locally as statistical extremes of the ozone climatology and not as air quality exceedances. With the University of California, Irvine chemistry-transport model (UCI CTM) we find there is skill in hindcasting these extreme episodes, and thus identify a new diagnostic using global chemistry–climate models (CCMs) to identify changes in the characteristics of extreme pollution episodes in a warming climate.