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Published in

American Meteorological Society, Journal of Climate, 19(28), p. 7764-7785, 2015

DOI: 10.1175/jcli-d-15-0106.1

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A Mechanism of Internal Decadal Atlantic Ocean Variability in a High-Resolution Coupled Climate Model

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Abstract The North Atlantic Ocean subpolar gyre (NA SPG) is an important region for initializing decadal climate forecasts. Climate model simulations and paleoclimate reconstructions have indicated that this region could also exhibit large, internally generated variability on decadal time scales. Understanding these modes of variability, their consistency across models, and the conditions in which they exist is clearly important for improving the skill of decadal predictions—particularly when these predictions are made with the same underlying climate models. This study describes and analyzes a mode of internal variability in the NA SPG in a state-of-the-art, high-resolution, coupled climate model. This mode has a period of 17 yr and explains 15%–30% of the annual variance in related ocean indices. It arises because of the advection of heat content anomalies around the NA SPG. Anomalous circulation drives the variability in the southern half of the NA SPG, while mean circulation and anomalous temperatures are important in the northern half. A negative feedback between Labrador Sea temperatures/densities and those in the North Atlantic Current (NAC) is identified, which allows for the phase reversal. The atmosphere is found to act as a positive feedback on this mode via the North Atlantic Oscillation (NAO), which itself exhibits a spectral peak at 17 yr. Decadal ocean density changes associated with this mode are driven by variations in temperature rather than salinity—a point which models often disagree on and which may affect the veracity of the underlying assumptions of anomaly-assimilating decadal prediction methodologies.