Millennial Variability in an Idealized Ocean Model: Predicting the AMOC Regime Shifts

Sévellec, Florian; Fedorov, Alexey V.

doi:10.1175/jcli-d-13-00450.1

Cited by 17 publications

(19 citation statements)

References 67 publications

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“…Unlike previous studies which aimed at predicting the AMOC variability (Hawkins and Sutton 2009;Robson et al 2012aRobson et al , 2014Sévellec and Fedorov 2014), we assume in this paper that we know the AMOC, and want to know what we can predict from this starting point.…”

Section: Methodsmentioning

confidence: 99%

Potential for seasonal prediction of Atlantic sea surface temperatures using the RAPID array at 26 $$^{\circ }$$ ∘ N

et al. 2015

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found that at a 5-month lag, the Ekman component mainly contributes to the southern part of the dipole and cumulative air-sea fluxes only explain a small fraction of the SSTA variability. Given that the southern part of the SSTA dipole encompasses the main development region for Atlantic hurricanes, our results therefore suggest the potential for AMOC observations from 26 • N to be used to complement existing seasonal hurricane forecasts in the Atlantic.

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Section: Methodsmentioning

confidence: 99%

Potential for seasonal prediction of Atlantic sea surface temperatures using the RAPID array at 26 $$^{\circ }$$ ∘ N

et al. 2015

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“…However, the magnitude (a few Sverdrups) and typical periodicity (decadal to centennial) of such variations mismatch the observed millennial variability during the past glacial‐interglacial interval. In contrast, a possibility of pronounced self‐sustained oscillations on a millennial timescale has been shown by many models of lower complexity, from box models (e.g., Colin de Verdière, ; Colin de Verdière et al, ) and loop models (mapping the ocean overturning circulation onto a rotating wheel; e.g., Sévellec & Fedorov, , ; Winton & Sarachik, ) to simplified coupled models (e.g., Sakai & Peltier, ; , ; Weaver & Hughes, ; Weijer & Dijkstra, ), suggesting that one or more of AMOC steady states may be unstable. This appears to be especially true for the models configured for glacial conditions.…”

Section: Implications Of the Amoc Stability Paradigm For The Past CLImentioning

confidence: 99%

“…This appears to be especially true for the models configured for glacial conditions. A plausible change in the AMOC behavior from the present-day climate to glacial conditions can be illustrated by a simple loop model of the AMOC (Sévellec & Fedorov, 2014, 2015Figure 9). The model assumes that the overturning circulation can be represented as a simple rotation on a latitude-depth plane and the flow is driven by a buoyancy torque balanced by friction.…”

Section: Intrinsically Generated Millennial Variabilitymentioning

confidence: 99%

Stability of the Atlantic Meridional Overturning Circulation: A Review and Synthesis

et al. 2019

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The notion that the Atlantic Meridional Overturning Circulation (AMOC) can have more than one stable equilibrium emerged in the 1980s as a powerful hypothesis to explain rapid climate variability during the Pleistocene. Ever since, the idea that a temporary perturbation of the AMOC—or a permanent change in its forcing—could trigger an irreversible collapse has remained a reason for concern. Here we review literature on the equilibrium stability of the AMOC and present a synthesis that puts our understanding of past and future AMOC behavior in a unifying framework. This framework is based on concepts from Dynamical Systems Theory, which has proven to be an important tool in interpreting a wide range of model behavior. We conclude that it cannot be ruled out that the AMOC in our current climate is in, or close to, a regime of multiple equilibria. But there is considerable uncertainty in the location of stability thresholds with respect to our current climate state, so we have no credible indications of where our present‐day AMOC is located with respect to thresholds. We conclude by identifying gaps in our knowledge and proposing possible ways forward to address these gaps.

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“…In this new model, the chaotic dynamics are generated internally by nonlinearities of the system. The model is a modification of the Howard-Malkus loop (Howard, 1971;Malkus, 1972;Welander, 1957Welander, , 1965Welander, , 1967, analogous to the waterwheel model (Dewar & Huang, 1995Strogatz, 1994), and representing AMOC dynamics (Sévellec & Fedorov, 2014. This model reads:…”

Section: Deterministic Frameworkmentioning

confidence: 99%

“…Hence, detection of early warning signals (Dakos et al, 2008;Lenton, 2011;Livina & Lenton, 2007;Scheffer et al, 2009) of an AMOC abrupt shutdown has become an extensive and fruitful area of current research. The research aims to determine precursors through statistical (Cimatoribus et al, 2013) or dynamical (Sévellec & Fedorov, 2014) approaches that assess the proximity of a dramatic AMOC event. In particular, it has been suggested that, whereas statistical early warning signals are weak (Cimatoribus et al, 2013), the ocean vertical stratification can be a good precursor of a dramatic collapse of the AMOC (Sévellec & Fedorov, 2014).…”

Section: Contributing Factorsmentioning

confidence: 99%

Predictability of Decadal Atlantic Meridional Overturning Circulation Variations

Sévellec¹,

Sinha²

2018

Oxford Research Encyclopedia of Climate Science

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The Atlantic meridional overturning circulation (AMOC) is a large, basin-scale circulation located in the Atlantic Ocean that transports climatically important quantities of heat northward. It can be described schematically as a northward flow in the warm upper ocean and a southward return flow at depth in much colder water. The heat capacity of a layer of 2 m of seawater is equivalent to that of the entire atmosphere; therefore, ocean heat content dominates Earth’s energy storage. For this reason and because of the AMOC’s typically slow decadal variations, the AMOC regulates North Atlantic climate and contributes to the relatively mild climate of Europe. Hence, predicting AMOC variations is crucial for predicting climate variations in regions bordering the North Atlantic. Similar to weather predictions, climate predictions are based on numerical simulations of the climate system. However, providing accurate predictions on such long timescales is far from straightforward. Even in a perfect model approach, where biases between numerical models and reality are ignored, the chaotic nature of AMOC variability (i.e., high sensitivity to initial conditions) is a significant source of uncertainty, limiting its accurate prediction. Predictability studies focus on factors determining our ability to predict the AMOC rather than actual predictions. To this end, processes affecting AMOC predictability can be separated into two categories: processes acting as a source of predictability (periodic harmonic oscillations, for instance) and processes acting as a source of uncertainty (small errors that grow and significantly modify the outcome of numerical simulations). To understand the former category, harmonic modes of variability or precursors of AMOC variations are identified. On the other hand, in a perfect model approach, the sources of uncertainty are characterized by the spread of numerical simulations differentiated by the application of small differences to their initial conditions. Two alternative and complementary frameworks have arisen to investigate this spread. The pragmatic framework corresponds to performing an ensemble of simulations, by imposing a randomly chosen small error on the initial conditions of individual simulations. This allows a probabilistic approach and to statistically characterize the importance of the initial condition by evaluating the spread of the ensemble. The theoretical framework uses stability analysis to identify small perturbations to the initial conditions, which are conducive to significant disruption of the AMOC. Beyond these difficulties in assessing the predictability, decadal prediction systems have been developed and tested through a range of hindcasts. The inherent difficulties of operational forecasts span from developing efficient initialization methods to setting accurate radiative forcing to correcting for model drift and bias, all these improvements being estimated and validated through a range of specifically designed skill metrics.

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Millennial Variability in an Idealized Ocean Model: Predicting the AMOC Regime Shifts

Cited by 17 publications

References 67 publications

Potential for seasonal prediction of Atlantic sea surface temperatures using the RAPID array at 26 $$^{\circ }$$ ∘ N

Potential for seasonal prediction of Atlantic sea surface temperatures using the RAPID array at 26 $$^{\circ }$$ ∘ N

Stability of the Atlantic Meridional Overturning Circulation: A Review and Synthesis

Predictability of Decadal Atlantic Meridional Overturning Circulation Variations

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