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Ocean Science An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/os-2017-31
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/os-2017-31
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Jun 2017

Research article | 06 Jun 2017

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This discussion paper is a preprint. A revision of the manuscript for further review has not been submitted.

Application of EnOI Assimilation in BCC_CSM1.1: Twin Experiments for Assimilating Sea Surface Data and T/S Profiles

Wei Zhou1, Jinghui Li2, Fang-Hua Xu2, and Yeqiang Shu1 Wei Zhou et al.
  • 1State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
  • 2Ministry of Education Key Laboratory for Earth System Modeling, and Department Earth System Science, Tsinghua University, Beijing 100084, China

Abstract. We applied an Ensemble Optimal Interpolation (EnOI) data assimilation method in the BCC_CSM1.1 to investigate the impact of ocean data assimilations on seasonal forecasts in an idealized twin-experiment framework. Pseudo-observations of sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity (T/S) profiles were first generated in a free model run. Then, a series of sensitivity tests initialized with predefined bias were conducted for a one-year period; this involved a free run (CTR) and seven assimilation runs. These tests allowed us to check the analysis field accuracy against the truth. As expected, data assimilation improved all investigated quantities; the joint assimilation of all variables gave more improved results than assimilating them separately. One-year predictions initialized from the seven runs and CTR were then conducted and compared. The forecasts initialized from joint assimilation of surface data produced comparable SST root mean square errors to that from assimilation of T/S profiles, but the assimilation of T/S profiles is crucial to reduce subsurface deficiencies. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents. The predictions of ocean heat content and mixed layer depth are significantly improved initialized from the joint assimilation of all the variables. Finally, a central Pacific El Niño was well predicted from the joint assimilation of surface data, indicating the importance of joint assimilation of SST, SSH, and SSS for ENSO predictions.

Wei Zhou et al.
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Status: closed (peer review stopped)
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Wei Zhou et al.
Wei Zhou et al.
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Short summary
we applied EnOI assimilation method with a global ocean model (MOM4.0) to estimate three-dimensional global ocean states when assimilating various variables, e.g., SST, SSH, SSS, and T/S profiles, in an idealized twin-experiment framework. The ocean surface currents in the tropics were better predicted when initial conditions produced by assimilating T/S profiles, while surface data assimilation became more important at higher latitudes, particularly near the western boundary currents.
we applied EnOI assimilation method with a global ocean model (MOM4.0) to estimate...
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