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An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part II: The Coupled Model

An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part II:... An improved forecast system has been developed and implemented for ENSO prediction at the National Centers for Environmental Prediction (NCEP). This system consists of a new ocean data assimilation system and an improved coupled ocean–atmosphere forecast model (CMP12) for ENSO prediction. The new ocean data assimilation system is described in Part I of this two-part paper. The new coupled forecast model (CMP12) is a variation of the standard NCEP coupled model (CMP10). Major changes in the new coupled model are improved vertical mixing for the ocean model; relaxation of the model’s surface salinity to the climatological annual cycle; and incorporation of an anomalous freshwater flux forcing. Also, the domain in which the oceanic SST couples to the atmosphere is limited to the tropical Pacific. Evaluation of ENSO prediction results show that the new coupled model, using the more accurate ocean initial conditions, achieves higher prediction skill. However, two sets of hindcasting experiments (one using the more accurate ocean initial conditions but the old coupled model, the other using the new coupled model but the less accurate ocean initial conditions), result in no improvement in prediction skill. These results indicate that future improvement in ENSO prediction skill requires systematically improving both the coupled model and the ocean analysis system. The authors’ results also suggest that for the purpose of initializing the coupled model for ENSO prediction, care should be taken to give sufficient weight to the model dynamics during the ocean data assimilation. This can reduce the danger of aliasing large-scale model biases into the low-frequency variability in the ocean initial conditions, and also reduce the introduction of small-scale noise into the initial conditions caused by overfitting the model to sparse observations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part II: The Coupled Model

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Publisher
American Meteorological Society
Copyright
Copyright © 1996 American Meteorological Society
ISSN
1520-0493
DOI
10.1175/1520-0493(1998)126<1022:AICMFE>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

An improved forecast system has been developed and implemented for ENSO prediction at the National Centers for Environmental Prediction (NCEP). This system consists of a new ocean data assimilation system and an improved coupled ocean–atmosphere forecast model (CMP12) for ENSO prediction. The new ocean data assimilation system is described in Part I of this two-part paper. The new coupled forecast model (CMP12) is a variation of the standard NCEP coupled model (CMP10). Major changes in the new coupled model are improved vertical mixing for the ocean model; relaxation of the model’s surface salinity to the climatological annual cycle; and incorporation of an anomalous freshwater flux forcing. Also, the domain in which the oceanic SST couples to the atmosphere is limited to the tropical Pacific. Evaluation of ENSO prediction results show that the new coupled model, using the more accurate ocean initial conditions, achieves higher prediction skill. However, two sets of hindcasting experiments (one using the more accurate ocean initial conditions but the old coupled model, the other using the new coupled model but the less accurate ocean initial conditions), result in no improvement in prediction skill. These results indicate that future improvement in ENSO prediction skill requires systematically improving both the coupled model and the ocean analysis system. The authors’ results also suggest that for the purpose of initializing the coupled model for ENSO prediction, care should be taken to give sufficient weight to the model dynamics during the ocean data assimilation. This can reduce the danger of aliasing large-scale model biases into the low-frequency variability in the ocean initial conditions, and also reduce the introduction of small-scale noise into the initial conditions caused by overfitting the model to sparse observations.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Oct 1, 1996

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