Articles | Volume 24, issue 2
Nonlin. Processes Geophys., 24, 125–139, 2017

Special issue: Current perspectives in modelling, monitoring, and predicting...

Nonlin. Processes Geophys., 24, 125–139, 2017

Research article 06 Mar 2017

Research article | 06 Mar 2017

Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study

Xiaolin Yu1, Shaoqing Zhang1,2, Xiaopei Lin1,2, and Mingkui Li1 Xiaolin Yu et al.
  • 1Physical Oceanography Laboratory of OUC, and Qingdao Collaborative Innovation Center of Marine Science and Technology, Qingdao, 266001, China
  • 2Function Laboratory for Ocean Dynamics and Climate, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266001, China

Abstract. 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 model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state–parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere with a chaotic nature is the major source of the inaccuracy of estimated state–parameter covariance. 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. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.

Short summary
Parameter estimation (PE) with a global coupled data assimilation (CDA) system can improve the runs, but the improvement remains in a limited range. We have to come back to simple models to sort out the sources of noises. Incomplete observations and the chaotic nature of the atmosphere have much stronger influences on the PE through the state estimation (SE) process. Here, we propose the guidelines of how to enhance the signal-to-noise ratio under partial SE status.