ENSO;
Forecast errors;
Error dynamics;
Spring predictability barrier;
ENSO predictability;
D O I:
暂无
中图分类号:
学科分类号:
摘要:
Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations (τx′)\documentclass[12pt]{minimal}
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\begin{document}$$(\tau_{x}^{\prime } )$$\end{document} near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic τx′\documentclass[12pt]{minimal}
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\begin{document}$$\tau_{x}^{\prime }$$\end{document} to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic τx′\documentclass[12pt]{minimal}
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\begin{document}$$\tau_{x}^{\prime }$$\end{document} is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall.
机构:
Univ Hawaii Manoa, Dept Atmospher Sci, Honolulu, HI 96822 USAUniv Hawaii Manoa, Dept Atmospher Sci, Honolulu, HI 96822 USA
Chen, Han-Ching
Tseng, Yu-Heng
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机构:
Natl Taiwan Univ, Inst Oceanog, Taipei, TaiwanUniv Hawaii Manoa, Dept Atmospher Sci, Honolulu, HI 96822 USA
Tseng, Yu-Heng
Hu, Zeng-Zhen
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机构:
NOAA NWS NCEP Climate Predict Ctr, College Pk, MD USAUniv Hawaii Manoa, Dept Atmospher Sci, Honolulu, HI 96822 USA
Hu, Zeng-Zhen
Ding, Ruiqiang
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机构:
Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R ChinaUniv Hawaii Manoa, Dept Atmospher Sci, Honolulu, HI 96822 USA
机构:
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of SciencesState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences
段晚锁
赵鹏
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机构:
China Meteorological Administration Training CenterState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics, Chinese Academy of Sciences
机构:
Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R ChinaChinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
Duan Wansuo
Zhao Peng
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机构:
China Meteorol Adm, Training Ctr, Beijing 100081, Peoples R ChinaChinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
Zhao Peng
Hu Junya
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机构:
Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
Univ Chinese Acad Sci, Beijing 100049, Peoples R ChinaChinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
Hu Junya
Xu Hui
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机构:
Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R ChinaChinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China
机构:Chinese Academy of Sciences,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics
Wansuo Duan
Peng Zhao
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机构:Chinese Academy of Sciences,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics
Peng Zhao
Junya Hu
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机构:Chinese Academy of Sciences,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics
Junya Hu
Hui Xu
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机构:Chinese Academy of Sciences,State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics