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ENSO Simulation and Prediction with a Hybrid Coupled Model

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  • 1 Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, Inc., Calverton, Maryland
  • | 2 Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York
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Abstract

A hybrid coupled model (HCM) consisting of a tropical Pacific Ocean and global atmosphere is presented. The ocean component is a linear reduced gravity model of the upper ocean in the tropical Pacific. The atmospheric component is a triangular 30 horizontal resolution global spectral general circulation model with 18 unevenly spaced levels in the vertical. In coupling these component models, an anomaly coupling strategy is employed. A 40-yr simulation was made with HCM and the variability in the tropical Pacific was compared to the observed variability. The HCM produces irregular ENSO events with a broad spectrum of periods between 12 and 48 months. On longer timescales, approximately 48 months, the simulated variability was weaker than the observed and on shorter timescales (approximately 24 months) the simulated variability was too strong. The simulated variability is asymmetric in the sense that the amplitude of the warm events is realistic, but there are no significant cold events.

An ensemble of 60 hindcast predictions was made with the HCM and the skill was compared to other prediction systems. In forecasting sea surface temperature anomalies in the eastern Pacific, the HCM is comparable to the other prediction systems for lead times up to 10 months. The anomaly correlation coefficient for the eastern Pacific SSTA remains above 0.6 for lead times of up to 11 months. Consistent with the 40-yr simulation, hindcasts of cold events have little skill, particularly when compared to hindcasts of warm events. Specific hindcasts also demonstrate that the HCM also has difficulty predicting the transition from warm conditions to normal or cold conditions.

Corresponding author address: Dr. Ben P. Kirtman, Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, Inc., 4041 Powdermill Road, Suite 302, Calverton, MD 20705-3106.

Email: kirtman@cola.iges.org

Abstract

A hybrid coupled model (HCM) consisting of a tropical Pacific Ocean and global atmosphere is presented. The ocean component is a linear reduced gravity model of the upper ocean in the tropical Pacific. The atmospheric component is a triangular 30 horizontal resolution global spectral general circulation model with 18 unevenly spaced levels in the vertical. In coupling these component models, an anomaly coupling strategy is employed. A 40-yr simulation was made with HCM and the variability in the tropical Pacific was compared to the observed variability. The HCM produces irregular ENSO events with a broad spectrum of periods between 12 and 48 months. On longer timescales, approximately 48 months, the simulated variability was weaker than the observed and on shorter timescales (approximately 24 months) the simulated variability was too strong. The simulated variability is asymmetric in the sense that the amplitude of the warm events is realistic, but there are no significant cold events.

An ensemble of 60 hindcast predictions was made with the HCM and the skill was compared to other prediction systems. In forecasting sea surface temperature anomalies in the eastern Pacific, the HCM is comparable to the other prediction systems for lead times up to 10 months. The anomaly correlation coefficient for the eastern Pacific SSTA remains above 0.6 for lead times of up to 11 months. Consistent with the 40-yr simulation, hindcasts of cold events have little skill, particularly when compared to hindcasts of warm events. Specific hindcasts also demonstrate that the HCM also has difficulty predicting the transition from warm conditions to normal or cold conditions.

Corresponding author address: Dr. Ben P. Kirtman, Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, Inc., 4041 Powdermill Road, Suite 302, Calverton, MD 20705-3106.

Email: kirtman@cola.iges.org

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