Skill assessment for ENSO using ensemble prediction

A. M. Moore, Richard Kleeman

Research output: Contribution to journalArticle

Abstract

A crucial component of any prediction system is the ability to estimate the predictive skill of a forecast so that the degree of confidence that can be placed in an individual forecast can be assessed. In this paper we have used ensemble prediction techniques to develop a means of estimating a priori the predictive skill of forecasts of El Niño Southern Oscillation (ENSO) using an intermediate coupled ocean-atmosphere model. Each member of an ensemble forecast is perturbed using either noise forcing fields or perturbations that are known to increase the low-frequency variability of the coupled model. These are respectively the so-called stochastic optimals and optimal perturbations of the coupled system; they are added to the model to mimic the presence of initial-condition errors and high-frequency stochastic noise and their effect on the predictability of the coupled system in the tropics. By performing ensemble predictions in hindcast mode, we have identified a usable relation between the skill of a model hindcast and the spread of the ensemble measured relative to some control hindcast. The practical nature of ensemble prediction is demonstrated by computing the relationship between model skill and the spread of an ensemble from hindcasts of ENSO for the period 1972-86, and then comparing the actual hindcast skills for the period 1987-93 with those suggested by the skill-spread relation from the preceding period. The relationship that we find between the model skill and spread of an ensemble appears to be robust in the sense that it is relatively insensitive to variations in the ensemble prediction procedure and to changes in some model parameters. However, crucial to the success of the ensemble predictions is the use of perturbations in the ensembles that are known to increase the low-frequency variability of the coupled model. These perturbations can efficiently probe the probability density function of possible coupled-model states. If randomly chosen perturbations are used in the ensembles, however, no practical relation between model skill and the spread of an ensemble emerges. The relationship identified here, between model skill and the spread of an ensemble prediction, offers a practical means of estimating the confidence that we can place in future forecasts of ENSO using the same coupled model.

Original languageEnglish (US)
Pages (from-to)557-584
Number of pages28
JournalQuarterly Journal of the Royal Meteorological Society
Volume124
Issue number546
StatePublished - Jan 1998

Fingerprint

Southern Oscillation
prediction
perturbation
probability density function
probe
forecast

Keywords

  • El Niño
  • Ensemble prediction
  • Optimal perturbations
  • Predictability

ASJC Scopus subject areas

  • Atmospheric Science

Cite this

Skill assessment for ENSO using ensemble prediction. / Moore, A. M.; Kleeman, Richard.

In: Quarterly Journal of the Royal Meteorological Society, Vol. 124, No. 546, 01.1998, p. 557-584.

Research output: Contribution to journalArticle

@article{244018292bd14b25b5f3cc9fdf7dc915,
title = "Skill assessment for ENSO using ensemble prediction",
abstract = "A crucial component of any prediction system is the ability to estimate the predictive skill of a forecast so that the degree of confidence that can be placed in an individual forecast can be assessed. In this paper we have used ensemble prediction techniques to develop a means of estimating a priori the predictive skill of forecasts of El Ni{\~n}o Southern Oscillation (ENSO) using an intermediate coupled ocean-atmosphere model. Each member of an ensemble forecast is perturbed using either noise forcing fields or perturbations that are known to increase the low-frequency variability of the coupled model. These are respectively the so-called stochastic optimals and optimal perturbations of the coupled system; they are added to the model to mimic the presence of initial-condition errors and high-frequency stochastic noise and their effect on the predictability of the coupled system in the tropics. By performing ensemble predictions in hindcast mode, we have identified a usable relation between the skill of a model hindcast and the spread of the ensemble measured relative to some control hindcast. The practical nature of ensemble prediction is demonstrated by computing the relationship between model skill and the spread of an ensemble from hindcasts of ENSO for the period 1972-86, and then comparing the actual hindcast skills for the period 1987-93 with those suggested by the skill-spread relation from the preceding period. The relationship that we find between the model skill and spread of an ensemble appears to be robust in the sense that it is relatively insensitive to variations in the ensemble prediction procedure and to changes in some model parameters. However, crucial to the success of the ensemble predictions is the use of perturbations in the ensembles that are known to increase the low-frequency variability of the coupled model. These perturbations can efficiently probe the probability density function of possible coupled-model states. If randomly chosen perturbations are used in the ensembles, however, no practical relation between model skill and the spread of an ensemble emerges. The relationship identified here, between model skill and the spread of an ensemble prediction, offers a practical means of estimating the confidence that we can place in future forecasts of ENSO using the same coupled model.",
keywords = "El Ni{\~n}o, Ensemble prediction, Optimal perturbations, Predictability",
author = "Moore, {A. M.} and Richard Kleeman",
year = "1998",
month = "1",
language = "English (US)",
volume = "124",
pages = "557--584",
journal = "Quarterly Journal of the Royal Meteorological Society",
issn = "0035-9009",
publisher = "John Wiley and Sons Ltd",
number = "546",

}

TY - JOUR

T1 - Skill assessment for ENSO using ensemble prediction

AU - Moore, A. M.

AU - Kleeman, Richard

PY - 1998/1

Y1 - 1998/1

N2 - A crucial component of any prediction system is the ability to estimate the predictive skill of a forecast so that the degree of confidence that can be placed in an individual forecast can be assessed. In this paper we have used ensemble prediction techniques to develop a means of estimating a priori the predictive skill of forecasts of El Niño Southern Oscillation (ENSO) using an intermediate coupled ocean-atmosphere model. Each member of an ensemble forecast is perturbed using either noise forcing fields or perturbations that are known to increase the low-frequency variability of the coupled model. These are respectively the so-called stochastic optimals and optimal perturbations of the coupled system; they are added to the model to mimic the presence of initial-condition errors and high-frequency stochastic noise and their effect on the predictability of the coupled system in the tropics. By performing ensemble predictions in hindcast mode, we have identified a usable relation between the skill of a model hindcast and the spread of the ensemble measured relative to some control hindcast. The practical nature of ensemble prediction is demonstrated by computing the relationship between model skill and the spread of an ensemble from hindcasts of ENSO for the period 1972-86, and then comparing the actual hindcast skills for the period 1987-93 with those suggested by the skill-spread relation from the preceding period. The relationship that we find between the model skill and spread of an ensemble appears to be robust in the sense that it is relatively insensitive to variations in the ensemble prediction procedure and to changes in some model parameters. However, crucial to the success of the ensemble predictions is the use of perturbations in the ensembles that are known to increase the low-frequency variability of the coupled model. These perturbations can efficiently probe the probability density function of possible coupled-model states. If randomly chosen perturbations are used in the ensembles, however, no practical relation between model skill and the spread of an ensemble emerges. The relationship identified here, between model skill and the spread of an ensemble prediction, offers a practical means of estimating the confidence that we can place in future forecasts of ENSO using the same coupled model.

AB - A crucial component of any prediction system is the ability to estimate the predictive skill of a forecast so that the degree of confidence that can be placed in an individual forecast can be assessed. In this paper we have used ensemble prediction techniques to develop a means of estimating a priori the predictive skill of forecasts of El Niño Southern Oscillation (ENSO) using an intermediate coupled ocean-atmosphere model. Each member of an ensemble forecast is perturbed using either noise forcing fields or perturbations that are known to increase the low-frequency variability of the coupled model. These are respectively the so-called stochastic optimals and optimal perturbations of the coupled system; they are added to the model to mimic the presence of initial-condition errors and high-frequency stochastic noise and their effect on the predictability of the coupled system in the tropics. By performing ensemble predictions in hindcast mode, we have identified a usable relation between the skill of a model hindcast and the spread of the ensemble measured relative to some control hindcast. The practical nature of ensemble prediction is demonstrated by computing the relationship between model skill and the spread of an ensemble from hindcasts of ENSO for the period 1972-86, and then comparing the actual hindcast skills for the period 1987-93 with those suggested by the skill-spread relation from the preceding period. The relationship that we find between the model skill and spread of an ensemble appears to be robust in the sense that it is relatively insensitive to variations in the ensemble prediction procedure and to changes in some model parameters. However, crucial to the success of the ensemble predictions is the use of perturbations in the ensembles that are known to increase the low-frequency variability of the coupled model. These perturbations can efficiently probe the probability density function of possible coupled-model states. If randomly chosen perturbations are used in the ensembles, however, no practical relation between model skill and the spread of an ensemble emerges. The relationship identified here, between model skill and the spread of an ensemble prediction, offers a practical means of estimating the confidence that we can place in future forecasts of ENSO using the same coupled model.

KW - El Niño

KW - Ensemble prediction

KW - Optimal perturbations

KW - Predictability

UR - http://www.scopus.com/inward/record.url?scp=0031779872&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031779872&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0031779872

VL - 124

SP - 557

EP - 584

JO - Quarterly Journal of the Royal Meteorological Society

JF - Quarterly Journal of the Royal Meteorological Society

SN - 0035-9009

IS - 546

ER -