Stochastically-forced multidecadal variability in the North Atlantic: a model study

Mecking, J. V.; Keenlyside, N. S.; Greatbatch, R. J.. 2014 Stochastically-forced multidecadal variability in the North Atlantic: a model study. Climate Dynamics, 43 (1-2). 271-288. https://doi.org/10.1007/s00382-013-1930-6

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Official URL: http://dx.doi.org/10.1007/s00382-013-1930-6

Abstract/Summary

Observations show a multidecadal signal in the North Atlantic ocean, but the underlying mechanism and cause of its timescale remain unknown. Previous studies have suggested that it may be driven by the North Atlantic Oscillation (NAO), which is the dominant pattern of winter atmospheric variability. To further address this issue, the global ocean general circulation model, Nucleus for European Modelling of the Ocean (NEMO), is driven using a 2,000 years long white noise forcing associated with the NAO. Focusing on key ocean circulation patterns, we show that the Atlantic Meridional Overturning Circulation (AMOC) and Sub-polar gyre (SPG) strength both have enhanced power at low frequencies but no dominant timescale, and thus provide no evidence for a oscillatory ocean-only mode of variability. Instead, both indices respond linearly to the NAO forcing, but with different response times. The variability of the AMOC at 30°N is strongly enhanced on timescales longer than 90 years, while that of the SPG strength starts increasing at 15 years. The different response characteristics are confirmed by constructing simple statistical models that show AMOC and SPG variability can be related to the NAO variability of the previous 53 and 10 winters, respectively. Alternatively, the AMOC and the SPG strength can be reconstructed with Auto-regressive (AR) models of order seven and five, respectively. Both statistical models reconstruct interannual and multidecadal AMOC variability well, while on the other hand, the AR(5) reconstruction of the SPG strength only captures multidecadal variability. Using these methods to reconstruct ocean variables can be useful for prediction and model intercomparision.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s00382-013-1930-6
ISSN:	0930-7575
Date made live:	21 Nov 2019 19:58 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/525951

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1007/s00382-013-1930-6

ISSN:

0930-7575

Date made live:

21 Nov 2019 19:58 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/525951