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Processes governing the predictability of the Atlantic meridional overturning circulation in a coupled GCM

Ortega, P., Hawkins, E. ORCID: https://orcid.org/0000-0001-9477-3677 and Sutton, R. ORCID: https://orcid.org/0000-0001-8345-8583 (2011) Processes governing the predictability of the Atlantic meridional overturning circulation in a coupled GCM. Climate Dynamics, 37 (9-10). pp. 1771-1782. ISSN 0930-7575

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To link to this item DOI: 10.1007/s00382-011-1025-1

Abstract/Summary

The processes that govern the predictability of decadal variations in the North Atlantic meridional overturning circulation (MOC) are investigated in a long control simulation of the ECHO-G coupled atmosphere–ocean model. We elucidate the roles of local stochastic forcing by the atmosphere, and other potential ocean processes, and use our results to build a predictive regression model. The primary influence on MOC variability is found to come from air–sea heat fluxes over the Eastern Labrador Sea. The maximum correlation between such anomalies and the variations in the MOC occurs at a lead time of 2 years, but we demonstrate that the MOC integrates the heat flux variations over a period of 10 years. The corresponding univariate regression model accounts for 74.5% of the interannual variability in the MOC (after the Ekman component has been removed). Dense anomalies to the south of the Greenland-Scotland ridge are also shown to precede the overturning variations by 4–6 years, and provide a second predictor. With the inclusion of this second predictor the resulting regression model explains 82.8% of the total variance of the MOC. This final bivariate model is also tested during large rapid decadal overturning events. The sign of the rapid change is always well represented by the bivariate model, but the magnitude is usually underestimated, suggesting that other processes are also important for these large rapid decadal changes in the MOC.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Science > School of Mathematical, Physical and Computational Sciences > NCAS
ID Code:19306
Publisher:Springer

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