Accessibility navigation


Skilful decadal predictions of subpolar North Atlantic SSTs using CMIP model-analogues

Menary, M. B., Mignot, J. and Robson, J. I. ORCID: https://orcid.org/0000-0002-3467-018X (2021) Skilful decadal predictions of subpolar North Atlantic SSTs using CMIP model-analogues. Environmental Research Letters. ISSN 1748-9326 (In Press)

[img] Text - Accepted Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.

836kB

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: 10.1088/1748-9326/ac06fb

Abstract/Summary

Predicting regional climate variability is a key goal of initialised decadal predictions and the North Atlantic has been a major focus due to its high level of predictability and potential impact on European climate. These predictions often focus on decadal variability in sea surface temperatures (SSTs) in the North Atlantic subpolar gyre (NA SPG). In order to understand the value of initialisation, and justify the high costs of such systems, predictions are routinely measured against technologically simpler benchmarks. Here, we present a new model-analogue benchmark that aims to leverage the latent information in uninitialised climate model simulations to make decadal predictions of NA SPG SSTs. This system searches through more than one hundred thousand simulated years in CMIP archives and yields skilful predictions in its target region comparable to initialised systems. Analysis of the underlying behaviour of the system suggests the origins of this skill are physically plausible. Such a system can provide a useful benchmark for initialised systems within the NA SPG and also suggests that the limits in initialised decadal prediction skill in this region have not yet been reached.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > NCAS
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:98477
Publisher:Institute of Physics

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

Page navigation