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Advances in Atmospheric Sciences

, Volume 33, Issue 7, pp 875–888 | Cite as

Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO analysis and prediction

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Abstract

A four-dimensional variational (4D-Var) data assimilation method is implemented in an improved intermediate coupled model (ICM) of the tropical Pacific. A twin experiment is designed to evaluate the impact of the 4D-Var data assimilation algorithm on ENSO analysis and prediction based on the ICM. The model error is assumed to arise only from the parameter uncertainty. The “observation” of the SST anomaly, which is sampled from a “truth” model simulation that takes default parameter values and has Gaussian noise added, is directly assimilated into the assimilation model with its parameters set erroneously. Results show that 4D-Var effectively reduces the error of ENSO analysis and therefore improves the prediction skill of ENSO events compared with the non-assimilation case. These results provide a promising way for the ICM to achieve better real-time ENSO prediction.

Key words

Four-dimensional variational data assimilation intermediate coupled model twin experiment ENSO prediction 
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Copyright information

© Authors 2016

Authors and Affiliations

  1. 1.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Marine Environmental Information Technology, State Oceanic AdministrationNational Marine Data and Information ServiceTianjinChina
  4. 4.Laboratory for Ocean and Climate DynamicsQingdao National Laboratory for Marine Science and TechnologyQingdaoChina

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