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Title: Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

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–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 the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.
Authors:
 [1] ;  [1] ;  [2] ;  [3] ;  [1] ;  [4] ;  [1]
  1. Univ. of Wisconsin-Madison, Madison, WI (United States)
  2. Princeton Univ., Princeton, NJ (United States)
  3. Argonne National Lab. (ANL), Argonne, IL (United States)
  4. Chinese Academy of Meteorological Sciences, Beijing (China)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 27; Journal Issue: 18; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
National Science Foundation (NSF), Directorate for Geosciences Division of Atmospheric and Geospace Sciences (GEO/OAD); USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; Climate models; Data assimilation; Optimization; Parameterization
OSTI Identifier:
1396288

Liu, Y., Liu, Z., Zhang, S., Jacob, R., Lu, F., Rong, X., and Wu, S.. Ensemble-Based Parameter Estimation in a Coupled General Circulation Model. United States: N. p., Web. doi:10.1175/JCLI-D-13-00406.1.
Liu, Y., Liu, Z., Zhang, S., Jacob, R., Lu, F., Rong, X., & Wu, S.. Ensemble-Based Parameter Estimation in a Coupled General Circulation Model. United States. doi:10.1175/JCLI-D-13-00406.1.
Liu, Y., Liu, Z., Zhang, S., Jacob, R., Lu, F., Rong, X., and Wu, S.. 2014. "Ensemble-Based Parameter Estimation in a Coupled General Circulation Model". United States. doi:10.1175/JCLI-D-13-00406.1. https://www.osti.gov/servlets/purl/1396288.
@article{osti_1396288,
title = {Ensemble-Based Parameter Estimation in a Coupled General Circulation Model},
author = {Liu, Y. and Liu, Z. and Zhang, S. and Jacob, R. and Lu, F. and Rong, X. and Wu, S.},
abstractNote = {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–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 the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Altogether, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.},
doi = {10.1175/JCLI-D-13-00406.1},
journal = {Journal of Climate},
number = 18,
volume = 27,
place = {United States},
year = {2014},
month = {9}
}