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Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion

Ho, C. K., Hawkins, E., Shaffrey, L., Broecker, J., Hermanson, L., Murphy, J. M., Smith, D. M. and Eade, R. (2013) Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion. Geophysical Research Letters, 40 (21). pp. 5770-5775. ISSN 0094-8276

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To link to this article DOI: 10.1002/2013GL057630

Abstract/Summary

Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e. forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, that the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System (DePreSys). Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be under-dispersed, and hence produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly over-dispersed. Such over-dispersion is primarily related to excessive inter-annual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Walker Institute
Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:35719
Uncontrolled Keywords:reliability;seasonal;decadal prediction;ensemble;dispersion
Publisher:American Geophysical Union

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