2019
DOI: 10.1029/2018gl081175
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Recent Acceleration of Arabian Sea Warming Induced by the Atlantic‐Western Pacific Trans‐basin Multidecadal Variability

Abstract: Arabian Sea (AS) warming has been significantly accelerated since the 1990s, in particular in the spring season. Here we link the AS warming changes to the Atlantic multidecadal oscillation (AMO). A set of Atlantic pacemaker experiments with a slab mixed‐layer ocean model successfully reproduces the AS spring multidecadal variability and its connection with the AMO. An atmospheric teleconnection from the Atlantic to the AS in the preceding winter and associated thermodynamic air‐sea feedback is found to be imp… Show more

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Cited by 67 publications
(26 citation statements)
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“…The SST cooling trend over the tropical central‐eastern Pacific largely projects on the negative IPO phase, and it is known that the IPO shifted from a positive phase to a negative phase in the late 1990s (Dong and McPhaden, 2017). In addition, due to the effects of trans‐basin atmospheric teleconnections and coupled air‐sea interaction, the tropical SST trend dipole can be linked to the Atlantic multidecadal variability (Li et al, ; McGregor et al, ; Sun et al, ; Sun, Li, Kucharski, Kang, et al, ), which is to a large extent a result of internal climate variability and induced by the ocean circulation variations (i.e., Atlantic meridional overturning circulation; Sun et al, ; Sun, Li, Kucharski, Xue, & Li, ; Zhang et al, ). Besides the Atlantic Ocean, the Indian Ocean warming in recent decades may also have an effect in shaping the tropical SST trend dipole pattern, leading to a cooling trend over the tropical eastern Pacific region (Luo et al, ; Lee et al, ; Nieves et al, ; Dong & McPhaden, ).…”
Section: Resultsmentioning
confidence: 99%
“…The SST cooling trend over the tropical central‐eastern Pacific largely projects on the negative IPO phase, and it is known that the IPO shifted from a positive phase to a negative phase in the late 1990s (Dong and McPhaden, 2017). In addition, due to the effects of trans‐basin atmospheric teleconnections and coupled air‐sea interaction, the tropical SST trend dipole can be linked to the Atlantic multidecadal variability (Li et al, ; McGregor et al, ; Sun et al, ; Sun, Li, Kucharski, Kang, et al, ), which is to a large extent a result of internal climate variability and induced by the ocean circulation variations (i.e., Atlantic meridional overturning circulation; Sun et al, ; Sun, Li, Kucharski, Xue, & Li, ; Zhang et al, ). Besides the Atlantic Ocean, the Indian Ocean warming in recent decades may also have an effect in shaping the tropical SST trend dipole pattern, leading to a cooling trend over the tropical eastern Pacific region (Luo et al, ; Lee et al, ; Nieves et al, ; Dong & McPhaden, ).…”
Section: Resultsmentioning
confidence: 99%
“…Similarly, the potential impacts of changes to SST on the WTV also need further examination. For example, we know that the Eurasian atmospheric circulation is not only influenced by Atlantic SST variability (Sun et al 2015(Sun et al , 2017a, but also by SST variability over the surrounding Indian Ocean and western tropical Pacific (Sun et al 2017b(Sun et al , 2019. This SST variability could play an important role in the formation and development of the internal dynamics associated with the WTV.…”
Section: Discussionmentioning
confidence: 99%
“…As an intermediate‐complexity numerical model, the SPEEDY AGCM is widely used in climate research and can help to understand and verify atmospheric responses to specific forcings (e.g., Kosovelj et al, 2019; Kucharski & Molteni, 2003; Kucharski, Molteni, & Yoo, 2006; Nnamchi et al, 2013). The SPEEDY AGCM has been widely used in studies related to NA SST‐associated atmospheric changes (e.g., Herceg‐Bulić & Kucharski, 2014; Kucharski et al, 2016; Kucharski & Molteni, 2003; Nnamchi et al, 2013; Sun et al, 2019). In this study, we use the SPEEDY AGCM to verify that the SST over the North Atlantic Ocean can excite a wave train propagating downstream to the TP after the mid‐1990s.…”
Section: Data Methods and Numerical Modelmentioning
confidence: 99%