Accessibility navigation

Comparing the UK Met Office climate prediction system DePreSys with idealized predictability in the HadCM3 model

Liu, C., Haines, K. ORCID:, Iwi, A. and Smith, D. (2012) Comparing the UK Met Office climate prediction system DePreSys with idealized predictability in the HadCM3 model. Quarterly Journal of the Royal Meteorological Society, 138 (662). pp. 81-90. ISSN 1477-870X (Part A)

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: 10.1002/qj.904


The initial condition effect on climate prediction skill over a 2-year hindcast time-scale has been assessed from ensemble HadCM3 climate model runs using anomaly initialization over the period 1990–2001, and making comparisons with runs without initialization (equivalent to climatological conditions), and to anomaly persistence. It is shown that the assimilation improves the prediction skill in the first year globally, and in a number of limited areas out into the second year. Skill in hindcasting surface air temperature anomalies is most marked over ocean areas, and is coincident with areas of high sea surface temperature and ocean heat content skill. Skill improvement over land areas is much more limited but is still detectable in some cases. We found little difference in the skill of hindcasts using three different sets of ocean initial conditions, and we obtained the best results by combining these to form a grand ensemble hindcast set. Results are also compared with the idealized predictability studies of Collins (Clim. Dynam. 2002; 19: 671–692), which used the same model. The maximum lead time for which initialization gives enhanced skill over runs without initialization varies in different regions but is very similar to lead times found in the idealized studies, therefore strongly supporting the process representation in the model as well as its use for operational predictions. The limited 12-year period of the study, however, means that the regional details of model skill should probably be further assessed under a wider range of observational conditions.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Environmental Systems Science Centre
ID Code:23634
Uncontrolled Keywords:climate prediction; DePreSys; skill; standard deviation
Publisher:Royal Meteorological Society

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

Page navigation