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Multidecadal variability in the Atlantic meridional overturning circulation (AMOC) is shown to differ significantly between the 4 × CO2 and preindustrial control simulations of the GFDL Earth System Model, version 2M (ESM2M) general circulation model (GCM). In the preindustrial simulation, this model has a peak in the power spectrum of both AMOC and northward heat transport at latitudes between 26° and 50°N. In the 4 × CO2 simulation, the only significant spectral peak is near 60°N. Understanding these differences is important for understanding the effect of future climate change on climate variability, as well as for providing insight into the physics underlying AMOC variability. Transfer function analysis demonstrates that the shift is predominantly due to a shift in the internal ocean dynamics rather than a change in stochastic atmospheric forcing. Specifically, the reduction in variance from 26° to 45°N is due to an increased stratification east of Newfoundland that results from the shallower and weaker mean overturning. The reduced AMOC variance that accompanies the reduced mean value of the AMOC at 4 × CO2 differs from predictions of simple box models that predict a weaker circulation to be closer to a stability bifurcation point and, therefore, be accompanied by amplified variability. The high-latitude variability in the 4 × CO2 simulation is related to the advection of anomalies by the subpolar gyre, distinct from the variability mechanism in the control simulation at lower latitudes. The 4 × CO2 variability has only a small effect on midlatitude meridional heat transport, but does significantly affect sea ice in the northern North Atlantic.

The authors describe a new method of comparing different climate forcing agents (e.g., CO2 concentration, CH4 concentration, and total solar irradiance) in climate models that circumvents many of the difficulties associated with explicit calculations of efficacy. This is achieved by introducing an explicit feedback loop external to a climate model that adjusts one forcing agent to balance another while keeping global-mean surface temperature constant. The convergence time of this feedback loop can be adjusted, allowing for comparisons of forcing agents to be achieved with relatively short simulations. Comparisons between forcing agents are highly linear in concordance with predicted scaling relationships; for example, the global-mean climate response to a doubling of the CO2 concentration is equivalent to that of a 2.1% change in total solar irradiance. This result is independent of the magnitude of the forcing agent (within the range of radiative forcings considered here) and is consistent across two different climate models.

The dynamics of the Atlantic meridional overturning circulation (AMOC) vary considerably among different climate models; for example, some models show clear peaks in their power spectra while others do not. To elucidate these model differences, transfer functions are used to estimate the frequency domain relationship between surface forcing fields, including sea surface temperature, salinity, and wind stress, and the resulting AMOC response. These are estimated from the outputs of the Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 3 (CMIP3) control runs for eight different models, with a specific focus on Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), and the Community Climate System Model, version 4 (CCSM4), which exhibit rather different spectral behavior. The transfer functions show very little agreement among models for any of the pairs of variables considered, suggesting the existence of systematic model errors and that considerable uncertainty in the simulation of AMOC in current climate models remains. However, a robust feature of the frequency domain analysis is that models with spectral peaks in their AMOC correspond to those in which AMOC variability is more strongly excited by high-latitude surface perturbations that have periods corresponding to the frequency of the spectral peaks. This explains why different models exhibit such different AMOC variability. These differences would not be evident without using a method that explicitly computes the frequency dependence rather than a priori assuming a particular functional form. Finally, transfer functions are used to evaluate two proposed physical mechanisms for model differences in AMOC variability: differences in Labrador Sea stratification and excitation by westward-propagating subsurface Rossby waves.