Ensemble-Based Parameter Estimation in a Coupled General Circulation Model
- Univ. of Wisconsin-Madison, Madison, WI (United States)
- Princeton Univ., Princeton, NJ (United States)
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Chinese Academy of Meteorological Sciences, Beijing (China)
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.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- National Science Foundation (NSF), Directorate for Geosciences Division of Atmospheric and Geospace Sciences (GEO/OAD); USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1396288
- Journal Information:
- Journal of Climate, Vol. 27, Issue 18; ISSN 0894-8755
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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