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

机译：通过概念性气候模型研究洞悉精确状态估计在耦合模型参数估计中的作用

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摘要

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 qatmosphere/q 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.
机译：耦合模型参数值的不确定性是引起模型气候漂移的模型偏差的重要来源。可以通过将观测信息投影到模型参数上的参数估计程序来校准这些值。模型状态与被估计参数之间的误差协方差的信噪比直接确定参数估计是否成功。利用将随机大气和慢变海洋耦合的概念性气候模型，本研究研究了状态参数协方差对耦合系统不同模型组件中估计模型状态准确性的敏感性。由于多个时间尺度的相互作用，具有混沌性质的快速变化的大气是估计的状态参数协方差不准确的主要原因。因此，提高大气状态的估计精度对于耦合模型参数估计的成功非常重要，尤其是对于海-海相互作用过程中的参数而言。还讨论了状态周期中混沌周期比率对参数估计的影响。当使用实际观测值来优化耦合的一般环流模型中的模型参数以改善气候分析和预测时，此简单的模型研究可提供指导。

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