Abstract
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.
Highlights
Nowadays, a coupled atmosphere–ocean general circulation model is widely used as a common tool in climate research and related applications
Note that what we describe here is a kind of observing system simulation experiment (OSSE; e.g., Tong and Xue, 2005; Jung et al, 2010)
The erroneous values of parameters in a coupled model are a source of model bias that can cause model climate drift
Summary
A coupled atmosphere–ocean general circulation model is widely used as a common tool in climate research and related applications. In the previous study with a conceptual coupled model, Zhang et al (2012) pointed out that an important aspect of successful coupled model parameter optimization is that the coupled model states must be sufficiently constrained by observations first This is because multiple sources of uncertainties exist in a coupled system consisting of different timescale media. Given the extreme importance of state–parameter covariance for PE, a clear answer for this question must further our understanding on coupled model parameter estimation To answer this question, this study uses a simple coupled model to examine the influence of observation-constrained states in each medium on PE for different parameters in different media thoroughly.
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