scholarly journals Ensemble Kalman Filter Parameter Estimation of Ocean Optical Properties for Reduced Biases in a Coupled General Circulation Model

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
V. Kitsios ◽  
P. Sandery ◽  
T. J. O’Kane ◽  
R. Fiedler
Keyword(s):  
Optical Properties ◽  
Parameter Estimation ◽  
Kalman Filter ◽  
General Circulation Model ◽  
Ensemble Kalman Filter ◽  
General Circulation ◽  
Circulation Model ◽  
Coupled General Circulation Model ◽  
Filter Parameter

scholarly journals Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

2014 ◽  
Vol 27 (18) ◽  
pp. 7151-7162 ◽  
Author(s):  
Y. Liu ◽  
Z. Liu ◽  
S. Zhang ◽  
R. Jacob ◽  
F. Lu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
General Circulation Model ◽  
General Circulation ◽  
Climate Models ◽  
Circulation Model ◽  
Single Parameter ◽  
Coupled General Circulation Model ◽  
Multiple Parameter ◽  
Fully Coupled ◽  
Multiple Parameter Estimation

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–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%. Overall, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.

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