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Potential Predictability of Sea Surface Temperature in a Coupled Ocean--Atmosphere GCM


doi: 10.1007/s00376-009-9062-y

  • Using the Flexible Global Ocean--Atmosphere--Land System model (FGOALS) version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature (SST), singular value decomposition (SVD) analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin (20oS--20oN, 120oE--80oW), respectively starting in four seasons from 1982 to 2005. On the basis of SVD analysis, the simulated pattern was replaced with the corresponding observed pattern to reconstruct SST anomaly fields to improve the ability of the simulation. The predictive skill, anomaly correlation coefficients (ACC), after systematic error correction using the first five modes was regarded as potential predictability. Results showed that: 1) the statistical postprocessing approach was effective for systematic error correction; 2) model error sources mainly arose from mode 2 extracted from the SVD analysis---that is, during the transition phase of ENSO, the model encountered the spring predictability barrier; and 3) potential predictability (upper limits of predictability) could be high over most of the tropical Pacific basin, including the tropical western Pacific and an extra 10-degrees region of the mid and eastern Pacific.
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    [2] Ben TIAN, Hong-Li REN, 2022: Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 427-442.  doi: 10.1007/s00376-021-1189-5
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Manuscript History

Manuscript received: 10 July 2010
Manuscript revised: 10 July 2010
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Potential Predictability of Sea Surface Temperature in a Coupled Ocean--Atmosphere GCM

  • 1. Institute of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044,National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: Using the Flexible Global Ocean--Atmosphere--Land System model (FGOALS) version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature (SST), singular value decomposition (SVD) analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin (20oS--20oN, 120oE--80oW), respectively starting in four seasons from 1982 to 2005. On the basis of SVD analysis, the simulated pattern was replaced with the corresponding observed pattern to reconstruct SST anomaly fields to improve the ability of the simulation. The predictive skill, anomaly correlation coefficients (ACC), after systematic error correction using the first five modes was regarded as potential predictability. Results showed that: 1) the statistical postprocessing approach was effective for systematic error correction; 2) model error sources mainly arose from mode 2 extracted from the SVD analysis---that is, during the transition phase of ENSO, the model encountered the spring predictability barrier; and 3) potential predictability (upper limits of predictability) could be high over most of the tropical Pacific basin, including the tropical western Pacific and an extra 10-degrees region of the mid and eastern Pacific.

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