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The influence of Atlantic variability on Asian summer climate is sensitive to the pattern of the sea surface temperature anomaly

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Abstract

We simulate the response of Asian summer climate to Atlantic multidecadal oscillation (AMO)-like sea surface temperature (SST) anomalies using an intermediate-complexity general circulation model (IGCM4). Experiments are performed with seven individualAMOSST anomalies obtained from CMIP5/PMIP3 global climate models as well as their multimodel mean, globally and over the North Atlantic Ocean only, for both the positive and negative phases of the AMO. During the positive (warm) AMO phase, a Rossby wave train propagates eastward, causing a high pressure and warm and dry surface anomalies over eastern China and Japan. During the negative (cool) phase of the AMO, the midlatitude Rossby wave train is less robust, but the model does simulate a warm and dry South Asian monsoon, associated with the movement of the intertropical convergence zone in the tropical Atlantic. The circulation response and associated temperature and precipitation anomalies are sensitive to the choice of AMO SST anomaly pattern. A comparison between global SST and North Atlantic SST perturbation experiments indicates that East Asian climate anomalies are forced from the North Atlantic region, whereas South Asian climate anomalies are more strongly affected by the AMO-related SST anomalies outside the North Atlantic region. Experiments conducted with different amplitudes of negative and positiveAMOanomalies show that the temperature response is linear with respect to SST anomaly but the precipitation response is nonlinear.

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Original languageEnglish
Pages (from-to)7567–7590
Number of pages24
JournalJournal of Climate
Volume33
Issue number17
Early online date23 Jun 2020
DOIs
Publication statusPublished - Sep 2020
Peer-reviewedYes

Bibliographic note

Data Availability Statement. The composite mean AMO SST patterns and the model-simulated data used for this manuscript are available at the Centre for Environmental Data Analysis (CEDA) archive at http://dx.doi.org/10.5285/3f2f92ad04f84e65a2399deeafd8bbbc

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