A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model
A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model
复制标题
DOI：
10.3402/tellusa.v64i0.10963
复制
发表时间：
2012-01
期刊：
影响因子：
--
通讯作者：
Shaoqing Zhang;Zhengyu Liu;A. Rosati;T. Delworth
中科院分区：
文献类型：
--
作者：
Shaoqing Zhang;Zhengyu Liu;A. Rosati;T. Delworth
耦合模式物理参数的不确定性是模式偏差的重要来源，对气候预测的初始化产生不利影响。利用模型动力学的误差协方差来提取观测信息的数据同化为优化参数值以减少这种偏差提供了一种很有前途的方法。然而，由于模型不确定性的多重来源，参数与模型状态之间的误差协方差往往带有噪声，因此在耦合模型中进行有效的参数估计通常是困难的。利用由3变量Lorenz模型和缓慢变化的平板“海洋”组成的简单耦合模型，首先研究了如何提高模型状态和参数协方差的信噪比，然后设计了一种增强参数校正（DAEPC）的数据同化方案。在DAEPC中，在状态估计达到“准平衡”后，参数估计才容易进行，其中耦合模型状态的不确定性受到观测值的充分约束，因此参数与模型状态之间的协方差是信号主导的。将观测值更新后的参数用于改进下一周期的状态估计，并对参数和模型状态的协方差进行了细化，进一步提高了参数的正确率。DAEPC具有快速收敛的动态自适应状态和参数估计，提供了一种系统的方法来估计整个阵列的耦合模型参数，并产生更准确的状态估计。预报实验表明，使用观测估计参数的DAEPC初始化极大地提高了模式的可预测性——有效的“大气”预报延长了两倍，而“海洋”预报几乎增加了两倍。本文的简单模式结果为改进耦合大气环流模式的气候估计和预测提供了一些见解。
ABSTRACT Uncertainties in physical parameters of coupled models are an important source of model bias and adversely impact initialisation for climate prediction. Data assimilation using error covariances derived from model dynamics to extract observational information provides a promising approach to optimise parameter values so as to reduce such bias. However, effective parameter estimation in a coupled model is usually difficult because the error covariance between a parameter and the model state tends to be noisy due to multiple sources of model uncertainties. Using a simple coupled model consisting of the 3-variable Lorenz model and a slowly varying slab ‘ocean’, this study first investigated how to enhance the signal-to-noise ratio in covariances between model states and parameters, and then designed a data assimilation scheme for enhancive parameter correction (DAEPC). In DAEPC, parameter estimation is facilitated after state estimation reaches a ‘quasi-equilibrium’ where the uncertainty of coupled model states is sufficiently constrained by observations so that the covariance between a parameter and the model state is signal dominant. The observation-updated parameters are applied to improving the next cycle of state estimation and the refined covariance of parameter and model state further improves parameter correction. Performing dynamically adaptive state and parameter estimations with speedy convergence, DAEPC provides a systematic way to estimate the whole array of coupled model parameters using observations, and produces more accurate state estimates. Forecast experiments show that the DAEPC initialisation with observation-estimated parameters greatly improves the model predictability – while valid ‘atmospheric’ forecasts are extended two times longer, the ‘oceanic’ predictability is almost tripled. The simple model results here provide some insights for improving climate estimation and prediction with a coupled general circulation model.