Accessibility navigation

Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice prediction systems

Hawkins, E. ORCID:, Tietsche, S., Day, J. J., Melia, N., Haines, K. ORCID: and Keeley, S. (2016) Aspects of designing and evaluating seasonal-to-interannual Arctic sea-ice prediction systems. Quarterly Journal of the Royal Meteorological Society, 142 (695). pp. 672-683. ISSN 1477-870X

Text (Open Access) - Accepted Version
· Please see our End User Agreement before downloading.


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.2643


Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter-annual Arctic sea-ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as: measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:41430
Additional Information:Part B
Publisher:Royal Meteorological Society


Downloads per month over past year

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

Page navigation