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Probabilistic skill in ensemble seasonal forecasts

Smith, Leonard A., Du, Hailiang, Suckling, Emma B. and Niehörster, Falk (2015) Probabilistic skill in ensemble seasonal forecasts. Quarterly Journal of the Royal Meteorological Society, 141 (689). pp. 1085-1100. ISSN 0035-9009

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Identification Number: 10.1002/qj.2403


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 DEMETER and the ENSEMBLES multi-model ensemble experiments is presented. Two particular regions are considered (Nino3.4 in the Pacific and Main Development Region in the Atlantic); these regions were chosen before any spatial distribution of skill were examined. The ENSEMBLES models are found to have skill against the climatological distribution on seasonal time scales. For models in ENSEMBLES which 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, 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 both from simulation models and from 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.

Item Type: Article
Official URL:
Additional Information: © 2015 The Authors © CC BY 4.0
Divisions: Statistics
Centre for Analysis of Time Series
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Date Deposited: 05 Jun 2014 09:57
Last Modified: 20 Oct 2021 00:31
Funders: Economic and Social Research Council, Munich Re

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