Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Raymond Sukhdeo, R. Grotjahn, Paul A. Ullrich
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引用次数: 0

Abstract

Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ∼10% of dry-spell events, are examined. The 500-hPa streamfunction anomaly fields for the first 12 days of each event are time averaged, and k-means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months. This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in nondrought months than in drought months, which means that dry spells can occur without preexisting drought conditions.
两种大规模气象模式与美国东北部的短时干旱现象有关
基于大尺度气象模式(LSMP)的分析以一种新颖的方式用于了解美国东北部短时干旱之前和期间的气象条件。这些 LSMP 有助于评估模型,并为未来预测选择性能更好的模型。旱灾事件是通过低于日降水量阈值的连续干旱天数柱状图确定的。对持续 12 天或更长时间的事件进行了研究,这些事件占旱灾事件的 10%。对每个事件前12天的500-hPa流函数异常场进行时间平均,并应用k-均值聚类法分离出与旱灾相关的LSMP。第一个聚类在该地区东南部的大西洋上空有较强的低气压异常,在冬季和春季较为常见。第二组在北美中东部上空有强高压,在秋季最为常见。在该地区上空,这两个集群都有负的比湿异常、来自北方的负综合水汽输送,以及与加强高层辐合的中纬度喷流偶极子结构有关的下沉。第一个气团的下沉得到冷空气平流的支持,第二个气团则位于低层高压的东侧。在干旱期,外热带气旋风暴轨道一般从该地区南移。单个事件位于两个截然不同的气旋群之间。这些群集具有相似的局部特性,但遥远特性不同。虽然旱灾在干旱月份发生的频率更高,但大多数旱灾都发生在非干旱月份。 本研究探讨了与影响美国东北部、持续至少两周的干旱现象相关的大尺度天气模式和气象条件。我们采用统计方法,根据相似的大气特征将旱灾事件分组。我们发现了两组不同的模式,我们称之为大尺度气象模式。这些模式减少了水汽,促进了局部下沉,并使风暴轨道沿大西洋沿岸南移,所有这些都减少了降水量。除了加深理解之外,了解该地区短期干旱期间的气象模式也是评估大气模式如何再现这些特定模式的重要工具。发生在非干旱月份的干旱现象多于干旱月份,这意味着干旱现象可以在没有预先存在干旱条件的情况下发生。
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来源期刊
Monthly Weather Review
Monthly Weather Review 地学-气象与大气科学
CiteScore
6.40
自引率
12.50%
发文量
186
审稿时长
3-6 weeks
期刊介绍: Monthly Weather Review (MWR) (ISSN: 0027-0644; eISSN: 1520-0493) publishes research relevant to the analysis and prediction of observed atmospheric circulations and physics, including technique development, data assimilation, model validation, and relevant case studies. This research includes numerical and data assimilation techniques that apply to the atmosphere and/or ocean environments. MWR also addresses phenomena having seasonal and subseasonal time scales.
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