• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nature >Butterfly effect and a self-modulating El Nino response to global warming
【24h】

Butterfly effect and a self-modulating El Nino response to global warming

机译：蝴蝶效应和自我调制的El Nino反应全球变暖

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Modelling experiments show that the El Nino response to global warming is self-modulating and depends on its historical variability; if current variability is high, future variability will be low.El Nino and La Nina, collectively referred to as the El Nino-Southern Oscillation (ENSO), are not only highly consequential(1-6)but also strongly nonlinear(7-14). For example, the maximum warm anomalies of El Nino, which occur in the equatorial eastern Pacific Ocean, are larger than the maximum cold anomalies of La Nina, which are centred in the equatorial central Pacific Ocean(7-9). The associated atmospheric nonlinear thermal damping cools the equatorial Pacific during El Nino but warms it during La Nina(15,16). Under greenhouse warming, climate models project an increase in the frequency of strong El Nino and La Nina events, but the change differs vastly across models(17), which is partially attributed to internal variability(18-23). Here we show that like a butterfly effect(24), an infinitesimal random perturbation to identical initial conditions induces vastly different initial ENSO variability, which systematically affects its response to greenhouse warming a century later. In experiments with higher initial variability, a greater cumulative oceanic heat loss from ENSO thermal damping reduces stratification of the upper equatorial Pacific Ocean, leading to a smaller increase in ENSO variability under subsquent greenhouse warming. This self-modulating mechanism operates in two large ensembles generated using two different models, each commencing from identical initial conditions but with a butterfly perturbation(24,25); it also operates in a large ensemble generated with another model commencing from different initial conditions(25,26)and across climate models participating in the Coupled Model Intercomparison Project(27,28). Thus, if the greenhouse-warming-induced increase in ENSO variability(29)is initially suppressed by internal variability, future ENSO variability is likely to be enhanced, and vice versa. This self-modulation linking ENSO variability across time presents a different perspective for understanding the dynamics of ENSO variability on multiple timescales in a changing climate.
机译：建模实验表明，对全球变暖的EL NINO反应是自我调制的，取决于其历史变异性;如果当前的变化很高，将来的变异性将是Low.el Nino和La Nina，统称为EL Nino-Southern振荡（ENSO），不仅是高度的（1-6），而且强烈地是非线性的（7-14 ）。例如，在赤道东太平洋发生的El Nino的最大温暖异常大于La Nina的最大冷异常，其位于赤道中央太平洋（7-9）。相关的大气非线性热阻尼在El Nino期间冷却赤道太平洋，但在La Nina（15,16）期间温暖它。在温室变暖下，气候模型项目增加了强大的El Nino和La Nino事件的频率，但变化跨越模型（17），部分归因于内部变异性（18-23）。在这里，我们表明，与蝴蝶效应（24）一样，与相同的初始条件相同的无限随机扰动会引起大量不同的初始enso可变性，系统地影响其对温室的反应以后变暖。在具有更高初始变异性的实验中，ENSO热阻尼的累积海洋热量较大降低了上赤道太平洋的分层，导致ZHSO可变异性较小，在温室变暖下的可变异性。这种自我调制机制在使用两种不同型号产生的两个大型集合中运行，每个大型初始条件从相同的初始条件开始，而是用蝴蝶扰动（24,25）;它还以与从不同初始条件（25,26）开始的另一模型生成的大型集合，并跨过参与耦合模型互通项目的气候模型（27,28）。因此，如果通过内部变异性最初抑制ENSO变异性（29）的温室加热诱导的增加，则可能会增强未来的ENSO可变性，反之亦然。这些自我调制链接跨时的enso可变性呈现出不同的视角，了解在变化气候中的多个时间尺度上的enso变异性的动态。

著录项

  • 来源
    《Nature》 |2020年第7823期|68-73|共6页
  • 作者

    Cai Wenju; Ng Benjamin; Geng Tao; Wu Lixin; Santoso Agus; McPhaden Michael J.;

  • 作者单位

    Ocean Univ China Key Lab Phys Oceanog Inst Adv Ocean Studies Qingdao Peoples R China|Qingdao Natl Lab Marine Sci & Technol Qingdao Peoples R China|CSIRO Oceans & Atmosphere Ctr Southern Hemisphere Oceans Res CSHOR Hobart Tas Australia;

    CSIRO Oceans & Atmosphere Ctr Southern Hemisphere Oceans Res CSHOR Hobart Tas Australia;

    Ocean Univ China Key Lab Phys Oceanog Inst Adv Ocean Studies Qingdao Peoples R China|Qingdao Natl Lab Marine Sci & Technol Qingdao Peoples R China|CSIRO Oceans & Atmosphere Ctr Southern Hemisphere Oceans Res CSHOR Hobart Tas Australia;

    Ocean Univ China Key Lab Phys Oceanog Inst Adv Ocean Studies Qingdao Peoples R China|Qingdao Natl Lab Marine Sci & Technol Qingdao Peoples R China;

    CSIRO Oceans & Atmosphere Ctr Southern Hemisphere Oceans Res CSHOR Hobart Tas Australia|Univ New South Wales Australian Res Council ARC Ctr Excellence Climate Extremes Sydney NSW Australia;

    NOAA Pacific Marine Environm Lab 7600 Sand Point Way Ne Seattle WA 98115 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号