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Probabilistic skill in ensemble seasonal forecasts

Smith, Leonard A.; Du, Hailiang; Suckling, Emma B.; Niehörster, Falk

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Leonard A. Smith

Emma B. Suckling

Falk Niehörster


Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add, either to purely empirical statistical models or to simpler simulation models. An evaluation of seasonal probability forecasts from the Development of a European Multimodel Ensemble system for seasonal to inTERannual prediction (DEMETER) and ENSEMBLES multi‐model ensemble experiments is presented. Two particular regions are considered: Nino3.4 in the Pacific and the Main Development Region in the Atlantic; these regions were chosen before any spatial distribution of skill was examined. The ENSEMBLES models are found to have skill against the climatological distribution on seasonal time‐scales. For models in ENSEMBLES that have a clearly defined predecessor model in DEMETER, the improvement from DEMETER to ENSEMBLES is discussed. Due to the long lead times of the forecasts and the evolution of observation technology, the forecast‐outcome archive for seasonal forecast evaluation is small; arguably, evaluation data for seasonal forecasting will always be precious. Issues of information contamination from in‐sample evaluation are discussed and impacts (both positive and negative) of variations in cross‐validation protocol are demonstrated. Other difficulties due to the small forecast‐outcome archive are identified. The claim that the multi‐model ensemble provides a ‘better’ probability forecast than the best single model is examined and challenged. Significant forecast information beyond the climatological distribution is also demonstrated in a persistence probability forecast. The ENSEMBLES probability forecasts add significantly more information to empirical probability forecasts on seasonal time‐scales than on decadal scales. Current operational forecasts might be enhanced by melding information from both simulation models and empirical models. Simulation models based on physical principles are sometimes expected, in principle, to outperform empirical models; direct comparison of their forecast skill provides information on progress toward that goal.


Smith, L. A., Du, H., Suckling, E. B., & Niehörster, F. (2015). Probabilistic skill in ensemble seasonal forecasts. Quarterly Journal of the Royal Meteorological Society, 141(689), 1085-1100.

Journal Article Type Article
Acceptance Date May 19, 2014
Online Publication Date Jul 16, 2014
Publication Date Apr 1, 2015
Deposit Date Jul 31, 2018
Publicly Available Date Aug 21, 2018
Journal Quarterly Journal of the Royal Meteorological Society
Print ISSN 0035-9009
Electronic ISSN 1477-870X
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 141
Issue 689
Pages 1085-1100
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Copyright Statement
© 2014 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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