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The Atlantic Multidecadal Oscillation (AMO) is a pronounced signal of climate variability in the North Atlantic sea-surface temperature field. In this paper, we propose an explanation of the physical processes responsible for the timescale and the spatial pattern of the AMO. Our approach involves the analysis of solutions of a hierarchy of models. In the lowest member of the model hierarchy, which is an ocean-only model for flow in an idealized basin, the variability shows up as a multidecadal oscillatory mode which is able to destabilize the mean thermohaline circulation. In the highest member of the model hierarchy, which is the Geophysical Fluid Dynamics Laboratory R30 climate model, multidecadal variability is found as a dominant statistical mode of variability. The connection between both results is established by tracing the spatial and temporal expression of the multidecadal mode through the model hierarchy while monitoring changes in specific quantities (mechanistic indicators) associated with its physics. The proposed explanation of the properties of the AMO is eventually based on the changes in the spatial patterns of variability through the model hierarchy.

The aim of this paper is to identify the physical mechanism of interdecadal variability in simulations of the North Atlantic Ocean circulation with the Modular Ocean Model of the Geophysical Fluid Dynamics Laboratory. To that end, a hierarchy of increasingly complex model configurations is used. The variability in the simplest case, that of viscous, purely thermally driven flows in a flat-bottom ocean basin with a box-shaped geometry, is shown to be caused by an internal interdecadal mode. The westward propagation of temperature anomalies and the phase difference between the anomalous zonal and meridional overturning that characterize the interdecadal mode are used as “fingerprints” of the physical mechanism of the variability. In this way, the variability can be followed toward a less viscous regime in which the effects of continental geometry and bottom topography are also included. It is shown that, although quantitative aspects of the variability like period and spatial pattern are changing, the physical mechanism of the interdecadal variability in the more complex simulations can be attributed to the same processes as in the simplest model configuration.

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