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North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

机译:北美极端降水事件及相关大型气象模式:审查统计方法,动态,建模和趋势

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摘要

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon-here, extreme precipitation-and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of "extreme" are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems-extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs-and several mechanisms-fronts, atmospheric rivers, and orographic ascent-have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.
机译:本文调查关于与北美短期(不到1周)极端降水事件相关的大规模气象模式(LSMP)的当前知识状态。与遥控器相比,通常基于气象场的特征空间变化或对已知强迫的远程循环响应的特征空间变化,相对于特定现象的发生定义了LSMP - 这里,极端降水 - 以及强调对个别事件产生主要影响的概率尺度,具有中等天气可预测性,并且在天气和气候模型中得到了很好的解决。对于具有极端降水的LSMP关系,我们考虑了以前的文献,了解定义和数据,动态机制,模型表示和气候变化趋势。在基于现有的观察降水数据和分析重新分析数据中的相关性能的一些限制,识别极端性具有相当大的不确定性。在使用“极端”的许多不同定义,使得难以直接比较不同的研究。动态地,几种类型的气象系统 - 卓越的旋风,热带气旋,Mescre对流系统和间隙 - 以及几种机制 - 河流,以及诸如极端降水LSMP的重要方面。通过组织,增强或由LSMP触发的Mescle过程来实现极端降水。理解LSMP的模型表示,趋势和投影处于早期阶段,尽管已经识别了一些有前途的分析技术并且LSMP的角度对于评估与极端相关联的模型动态有用。

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  • 来源
    《Climate dynamics》 |2019年第11期|6835-6875|共41页
  • 作者单位

    Univ Massachusetts Lowell MA 02155 USA;

    Iowa State Univ Dept Geol & Atmospher Sci Ames IA 50011 USA;

    McGill Univ Dept Atmospher & Ocean Sci Montreal PQ Canada;

    Natl Ctr Atmospher Res POB 3000 Boulder CO 80307 USA;

    NASA Goddard Space Flight Ctr Global Modeling & Assimilat Off Greenbelt MD 20771 USA|Goddard Earth Sci Technol & Res IM Syst Grp Greenbelt MD 20771 USA;

    Colorado State Univ Ft Collins CO 80523 USA;

    Lawrence Berkeley Natl Lab Berkeley CA 94720 USA;

    Univ Massachusetts Lowell MA 02155 USA;

    NASA GSFC Global Modeling & Assimilat Off Greenbelt MD 20771 USA;

    NASA GSFC Global Modeling & Assimilat Off Greenbelt MD 20771 USA|Univ Space Res Assoc Greenbelt MD 20771 USA;

    Univ Calif San Diego Scripps Inst Oceanog Climate Atmospher Sci & Phys Oceanog CASPO Div La Jolla CA 92093 USA;

    Univ Calif Davis Atmospher Sci Program Dept LAWR One Shields Ave Davis CA 95616 USA;

    Pacific Northwest Natl Lab Richland WA 99352 USA;

    Embry Riddle Aeronaut Univ Appl Aviat Sci Dept Meteorol Program Daytona Beach FL 32114 USA;

    Pohang Univ Sci & Technol Div Environm Sci & Engn Pohang 37673 Gyeongbuk South Korea;

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