Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems

Sévellec, Florian; Dijkstra, Henk A.; Drijfhout, Sybren S.; Germe, Agathe. 2017 Dynamical attribution of oceanic prediction uncertainty in the North Atlantic: application to the design of optimal monitoring systems. Climate Dynamics, 51 (4). 1517-1535. https://doi.org/10.1007/s00382-017-3969-2

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Official URL: http://dx.doi.org/10.1007/s00382-017-3969-2

Abstract/Summary

In this study, the relation between two approaches to assess the ocean predictability on interannual to decadal time scales is investigated. The first pragmatic approach consists of sampling the initial condition uncertainty and assess the predictability through the divergence of this ensemble in time. The second approach is provided by a theoretical framework to determine error growth by estimating optimal linear growing modes. In this paper, it is shown that under the assumption of linearized dynamics and normal distributions of the uncertainty, the exact quantitative spread of ensemble can be determined from the theoretical framework. This spread is at least an order of magnitude less expensive to compute than the approximate solution given by the pragmatic approach. This result is applied to a state-of-the-art Ocean General Circulation Model to assess the predictability in the North Atlantic of four typical oceanic metrics: the strength of the Atlantic Meridional Overturning Circulation (AMOC), the intensity of its heat transport, the two-dimensional spatially-averaged Sea Surface Temperature (SST) over the North Atlantic, and the three-dimensional spatially-averaged temperature in the North Atlantic. For all tested metrics, except for SST,

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s00382-017-3969-2
ISSN:	0930-7575
Date made live:	12 Dec 2017 16:30 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/518681

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1007/s00382-017-3969-2

ISSN:

0930-7575

Date made live:

12 Dec 2017 16:30 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/518681