Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Does the Predictability of ENSO Depend on the Seasonal Cycle?

Does the Predictability of ENSO Depend on the Seasonal Cycle? 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Atmospheric Sciences American Meteorological Society

Does the Predictability of ENSO Depend on the Seasonal Cycle?

Loading next page...
 
/lp/american-meteorological-society/does-the-predictability-of-enso-depend-on-the-seasonal-cycle-pbKeN0rL3a
Publisher
American Meteorological Society
Copyright
Copyright © 1997 American Meteorological Society
ISSN
1520-0469
DOI
10.1175/1520-0469(1998)055<3230:DTPOED>2.0.CO;2
Publisher site
See Article on Publisher Site

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.

Journal

Journal of the Atmospheric SciencesAmerican Meteorological Society

Published: Apr 17, 1997

There are no references for this article.