Accessibility navigation


Estimating climatically relevant singular vectors for decadal predictions of the Atlantic Ocean

Hawkins, E. ORCID: https://orcid.org/0000-0001-9477-3677 and Sutton, R. ORCID: https://orcid.org/0000-0001-8345-8583 (2011) Estimating climatically relevant singular vectors for decadal predictions of the Atlantic Ocean. Journal of Climate, 24 (1). pp. 109-123. ISSN 1520-0442

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1175/2010JCLI3579.1

Abstract/Summary

A key aspect in designing an ecient decadal prediction system is ensuring that the uncertainty in the ocean initial conditions is sampled optimally. Here, we consider one strategy to address this issue by investigating the growth of optimal perturbations in the HadCM3 global climate model (GCM). More specically, climatically relevant singular vectors (CSVs) - the small perturbations which grow most rapidly for a specic initial condition - are estimated for decadal timescales in the Atlantic Ocean. It is found that reliable CSVs can be estimated by running a large ensemble of integrations of the GCM. Amplication of the optimal perturbations occurs for more than 10 years, and possibly up to 40 years. The identi ed regions for growing perturbations are found to be in the far North Atlantic, and these perturbations cause amplication through an anomalous meridional overturning circulation response. Additionally, this type of analysis potentially informs the design of future ocean observing systems by identifying the sensitive regions where small uncertainties in the ocean state can grow maximally. Although these CSVs are expensive to compute, we identify ways in which the process could be made more ecient in the future.

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:16486
Publisher:American Meteorological Society

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation