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

Ho, C. K., Hawkins, E. ORCID: https://orcid.org/0000-0001-9477-3677, Shaffrey, L. ORCID: https://orcid.org/0000-0003-2696-752X, 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 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
URI	https://reading-clone.eprints-hosting.org/id/eprint/34802
Identification Number/DOI	10.1002/2013GL057630
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Walker Institute Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords	reliability;seasonal;decadal prediction;ensemble;dispersion
Publisher	American Geophysical Union
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• Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion. (deposited 08 Nov 2013 13:16) [Currently Displayed]
  • Examining reliability of seasonal to decadal sea surface temperature forecasts: the role of ensemble dispersion. (deposited 17 Jan 2014 15:43)