How the AMOC affects ocean temperatures on decadal to centennial timescales: the North Atlantic versus an interhemispheric seesaw
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This study investigates how variations of the Atlantic meridional overturning circulation (AMOC) affect sea surface temperature (SST) within the simulations of the coupled model intercomparison project phase 5. In particular, we explore whether the SST response is interhemispheric in nature, specifically as reflected in the Atlantic SST Dipole index, or whether the response is localized more in the North Atlantic Ocean. In the absence of direct observational data, this Dipole index has been proposed to approximate AMOC variations over the duration of the instrumental temperature record. We find that typically, on timescales between decadal and centennial, the SST Dipole index correlates with the AMOC with coefficients ranging from 0.2 to 0.7, typically with a 0–6 year lag, and thus explains less than half of the AMOC variance. In just two models this value slightly exceeds 50 %. Even for the models with the highest correspondence between the AMOC and the Dipole index, the correlation between the two variables is controlled mainly by SST variations in the North Atlantic, not the South Atlantic, both for the model control and historical simulations. Consequently, in nearly all models, the North Atlantic SST provides a better indicator of AMOC variations than the Atlantic Dipole. Thus, on decadal to centennial timescales AMOC variability affects mainly the North Atlantic Ocean, with the sensitivity of the North Atlantic SST between 40 and 60°N, given by the multi-model average, of about 0.3 °C per 1 Sv of AMOC change, explaining roughly one third of the SST variance.
KeywordsAMOC SST Dipole AOGCMs
This research was supported by Grants from DOE Office of Science (DE-SC0007037) and NSF (AGS-1405272). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Support from the Yale University Faculty of Arts and Sciences High Performance Computing facility is also acknowledged. We would also like to acknowledge the useful and detailed comments from anonymous reviewers of both the current and a previous version of this paper.
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