@article{101096,
  keywords = {optimization, data assimilation, Climate models, Parameterization},
  author = {Yun Liu and Zhengyu Liu and Shaoqing Zhang and Robert Jacob and Feiyu Lu and Rong and Wu},
  title = {Ensemble-Based Parameter Estimation in a Coupled General Circulation Model},
  abstract = { Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean{\textendash}atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90\% after \~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of ... },
  year = {2014},
  journal = {Journal of Climate},
  volume = {27},
  pages = {7151{\textendash}7162},
  issn = {0894-8755},
  url = {http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00406.1},
  doi = {10.1175/JCLI-D-13-00406.1},
  language = {eng},
}