Further Insights on the Role of Accurate State Estimation in Coupled Model Parameter Estimation by a Simple Climate Model Study

The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto parameters. The signal-to-noise ratio of error covariance between model states and initially perturbed parameters determinates directly the success of parameter estimation or not. With a conceptual climate model that couples the stochastic atmosphere and slow varying ocean, this study examines the sensitivity of the state-parameter covariance on the accuracy of 15 estimated model states in different model components of a coupled system. Due to the interaction of multiple time scales, the fast varying “atmosphere” with the chaotic nature is the major source of state-parameter covariance uncertainties, and thus enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed in this study. This simple model study provides a guideline when real 20 observations are used to optimize model parameter in a coupled general circulation model for improving climate analysis and predictions. 25 Nonlin. Processes Geophys. Discuss., doi:10.5194/npg-2016-52, 2016 Manuscript under review for journal Nonlin. Processes Geophys. Published: 27 September 2016 c © Author(s) 2016. CC-BY 3.0 License.