The Potential Predictability in a 14-Year GCM Simulation

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  • 1 Climate Analysis Center, National Meteorological Center, Washington, D.C.
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Abstract

A 14-yr simulation of a GCM forced by observed SST and sea ice is compared with observations as well as a GCM simulation that used climatological surface conditions. The low frequency (periods > 2 months) behavior in both simulations and observations is examined, and it is found that the anomalous boundary conditions were the cause of much of the low-frequency variability in the simulations. Without the anomalous boundary conditions, the low-frequency spectra was often flat, suggesting that the internal variability was producing a white noise-like spectra. The anomalous boundary conditions were found to be very important in determining the low-frequency behavior of the model. If the future values of the SST and sea ice were known, then the predictability for certain variables could be quite high for low-frequency signals (periods > 3 months). Specific zones showed predictability for low-frequency signals in excess of 70% explained variance. These zones were often related to ENSO, as the Southern Oscillation is the strongest intradecadal phenomenon that is forced by the anomalous boundary conditions. This study gives a lower bound on the variance explained by the anomalous surface forcings.

Abstract

A 14-yr simulation of a GCM forced by observed SST and sea ice is compared with observations as well as a GCM simulation that used climatological surface conditions. The low frequency (periods > 2 months) behavior in both simulations and observations is examined, and it is found that the anomalous boundary conditions were the cause of much of the low-frequency variability in the simulations. Without the anomalous boundary conditions, the low-frequency spectra was often flat, suggesting that the internal variability was producing a white noise-like spectra. The anomalous boundary conditions were found to be very important in determining the low-frequency behavior of the model. If the future values of the SST and sea ice were known, then the predictability for certain variables could be quite high for low-frequency signals (periods > 3 months). Specific zones showed predictability for low-frequency signals in excess of 70% explained variance. These zones were often related to ENSO, as the Southern Oscillation is the strongest intradecadal phenomenon that is forced by the anomalous boundary conditions. This study gives a lower bound on the variance explained by the anomalous surface forcings.

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