Is There Evidence of Changes in Tropical Atlantic Variability Modes under AMO Phases in the Observational Record?

大西洋年代际振荡 热带大西洋 气候学 大西洋赤道模 遥相关 温跃层 温带气旋 北大西洋涛动 海洋环流 海面温度 北大西洋深水区 地质学 海洋学 环境科学 温盐循环 亚热带 厄尔尼诺南方涛动 生物 生态学
作者
Marta Martín-Rey,Irene Polo,Belén Rodríguez-Fonseca,Teresa Losada,Alban Lazar
出处
期刊:Journal of Climate [American Meteorological Society]
卷期号:31 (2): 515-536 被引量:56
标识
DOI:10.1175/jcli-d-16-0459.1
摘要

The Atlantic multidecadal oscillation (AMO) is the leading mode of Atlantic sea surface temperature (SST) variability at multidecadal time scales. Previous studies have shown that the AMO could modulate El Niño–Southern Oscillation (ENSO) variance. However, the role played by the AMO in the tropical Atlantic variability (TAV) is still uncertain. Here, it is demonstrated that during negative AMO phases, associated with a shallower thermocline, the eastern equatorial Atlantic SST variability is enhanced by more than 150% in boreal summer. Consequently, the interannual TAV modes are modified. During negative AMO, the Atlantic Niño displays larger amplitude and a westward extension and it is preceded by a simultaneous weakening of both subtropical highs in winter and spring. In contrast, a meridional seesaw SLP pattern evolving into a zonal gradient leads the Atlantic Niño during positive AMO. The north tropical Atlantic (NTA) mode is related to a Scandinavian blocking pattern during winter and spring in negative AMO, while under positive AMO it is part of the SST tripole associated with the North Atlantic Oscillation. Interestingly, the emergence of an overlooked variability mode, here called the horseshoe (HS) pattern on account of its shape, is favored during negative AMO. This anomalous warm (cool) HS surrounding an eastern equatorial cooling (warming) is remotely forced by an ENSO phenomenon. During negative AMO, the tropical–extratropical teleconnections are enhanced and the Walker circulation is altered. This, together with the increased equatorial SST variability, could promote the ENSO impacts on TAV. The results herein give a step forward in the better understanding of TAV, which is essential to improving its modeling, impacts, and predictability.
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