Lin Meiyun, Fiore Arlene M, Horowitz Larry W, Langford Andrew O, Oltmans Samuel J, Tarasick David, Rieder Harald E
1] Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey 08540, USA [2] NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey 08540, USA.
1] Department of Earth and Environmental Sciences, Columbia University, New York City, New York 10027, USA [2] Lamont-Doherty Earth-Observatory, Columbia University, Palisades, New York 10964, USA.
Nat Commun. 2015 May 12;6:7105. doi: 10.1038/ncomms8105.
Evidence suggests deep stratospheric intrusions can elevate western US surface ozone to unhealthy levels during spring. These intrusions can be classified as 'exceptional events', which are not counted towards non-attainment determinations. Understanding the factors driving the year-to-year variability of these intrusions is thus relevant for effective implementation of the US ozone air quality standard. Here we use observations and model simulations to link these events to modes of climate variability. We show more frequent late spring stratospheric intrusions when the polar jet meanders towards the western United States, such as occurs following strong La Niña winters (Niño3.4<-1.0 °C). While El Niño leads to enhancements of upper tropospheric ozone, we find this influence does not reach surface air. Fewer and weaker intrusion events follow in the two springs after the 1991 volcanic eruption of Mt. Pinatubo. The linkage between La Niña and western US stratospheric intrusions can be exploited to provide a few months of lead time during which preparations could be made to deploy targeted measurements aimed at identifying these exceptional events.
有证据表明，平流层深层侵入事件会在春季将美国西部地表臭氧浓度提升至不健康水平。这些侵入事件可被归类为“异常事件”，在判定未达标情况时不计入其中。因此，了解驱动这些侵入事件年际变化的因素对于有效实施美国臭氧空气质量标准至关重要。在此，我们利用观测数据和模型模拟将这些事件与气候变率模式联系起来。我们发现，当极地急流向美国西部蜿蜒时，晚春平流层侵入事件更为频繁，比如在强烈的拉尼娜冬季（尼诺3.4指数＜-1.0°C）之后就会出现这种情况。虽然厄尔尼诺现象会导致对流层上层臭氧增加，但我们发现这种影响不会波及地表空气。1991年皮纳图博火山喷发后的两个春季，侵入事件较少且强度较弱。拉尼娜现象与美国西部平流层侵入事件之间的联系可被利用，以提前几个月提供预警时间，在此期间可进行准备，部署有针对性的测量，旨在识别这些异常事件。
