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Does the Predictability of ENSO Depend on the Seasonal Cycle?

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  • 1 Department of Oceanography, Texas A&M University, College Station, Texas
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Abstract

An intermediate coupled ocean–atmosphere model that permits dynamical interactions between the seasonal cycle and interannual oscillations is used to conduct large ensembles of ENSO prediction experiments. By varying seasonal backgrounds, the impact of the annual cycle on the model’s forecast skills is explored. The results show that the sensitivity of the skills to changes in the seasonal cycle is weak, although correlation skills drop and rms errors increase systematically by a small amount as the amplitude of the seasonal cycle is enhanced. This suggests that the nature of the model’s prediction skills is largely determined by the seasonal information hidden in the initial conditions and the actual varying seasonal background is of secondary importance.

As in other anomaly coupled models, the spring predictability barrier is a predominant feature of this model’s prediction skills. This seasonal dependence of the forecast skills exhibits a decadal modulation with strong barriers in the 1960s and 1970s and weak ones in the 1950s and 1980s. The best skills of the model occur in the 1950s and 1980s and the worst in the 1970s. The decadal modulation of the skills is more likely to come from decadal shifts in the mean state of the tropical Pacific than from nonlinear interactions between the seasonal cycle and interannual oscillations.

Corresponding author address: Dr. Moritz Flügel, Department of Oceanography, Texas A&M University, College Station, TX 77843-3146.

Email: fluegel@mako.tamu.edu

Abstract

An intermediate coupled ocean–atmosphere model that permits dynamical interactions between the seasonal cycle and interannual oscillations is used to conduct large ensembles of ENSO prediction experiments. By varying seasonal backgrounds, the impact of the annual cycle on the model’s forecast skills is explored. The results show that the sensitivity of the skills to changes in the seasonal cycle is weak, although correlation skills drop and rms errors increase systematically by a small amount as the amplitude of the seasonal cycle is enhanced. This suggests that the nature of the model’s prediction skills is largely determined by the seasonal information hidden in the initial conditions and the actual varying seasonal background is of secondary importance.

As in other anomaly coupled models, the spring predictability barrier is a predominant feature of this model’s prediction skills. This seasonal dependence of the forecast skills exhibits a decadal modulation with strong barriers in the 1960s and 1970s and weak ones in the 1950s and 1980s. The best skills of the model occur in the 1950s and 1980s and the worst in the 1970s. The decadal modulation of the skills is more likely to come from decadal shifts in the mean state of the tropical Pacific than from nonlinear interactions between the seasonal cycle and interannual oscillations.

Corresponding author address: Dr. Moritz Flügel, Department of Oceanography, Texas A&M University, College Station, TX 77843-3146.

Email: fluegel@mako.tamu.edu

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