A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model

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• 作者：Xinrong Wu ; Guijun Han ; Shaoqing Zhang ; Zhengyu Liu
• 关键词：Parameter optimization ; Ensemble Kalman filter ; ENSO predictability ; Intermediate coupled model
• 刊名：Climate Dynamics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：46
• 期：3-4
• 页码：711-727
• 全文大小：3,220 KB
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• 作者单位：Xinrong Wu (1)
  Guijun Han (1)
  Shaoqing Zhang (2)
  Zhengyu Liu (3) (4)
  
  1. Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, People’s Republic of China
  2. GFDL/NOAA, Princeton University, Princeton, NJ, USA
  3. Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WS, USA
  4. Lab. of Ocean-Atmos. Studies, Peking University, Beijing, People’s Republic of China
  
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Geophysics and Geodesy
  Meteorology and Climatology
  Oceanography
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1432-0894

文摘

Model error is a major obstacle for enhancing the forecast skill of El Niño-Southern Oscillation (ENSO). Among three kinds of model error sources—dynamical core misfitting, physical scheme approximation and model parameter errors, the model parameter errors are treatable by observations. Based on the Zebiak-Cane model, an ensemble coupled data assimilation system is established to study the impact of parameter optimization (PO) on ENSO predictions within a biased twin experiment framework. “Observations” of sea surface temperature anomalies drawn from the “truth” model are assimilated into a biased prediction model in which model parameters are erroneously set from the “truth” values. The degree by which the assimilation and prediction with or without PO recover the “truth” is a measure of the impact of PO. Results show that PO improves ENSO predictability—enhancing the seasonal-interannual forecast skill by about 18 %, extending the valid lead time up to 33 % and ameliorating the spring predictability barrier. Although derived from idealized twin experiments, results here provide some insights when a coupled general circulation model is initialized from the observing system. Keywords Parameter optimization Ensemble Kalman filter ENSO predictability Intermediate coupled model