Potential Predictability of Sea Surface Temperature in a Coupled Ocean--Atmosphere GCM |
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| Authors: | YAN Li WANG Panxing YU Yongqiang LI Lijuan and WANG Bin |
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| Institution: | 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 |
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| 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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| Keywords: | coupled GCM hindcasting experiments SST singular value decomposition potential predictability |
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