Abstract
Abstract. Imperfect dynamical core is an important source of model biases that adversely impact on the model simulation and predictability of a coupled system. With a simple pycnocline prediction model, in this study, we show the mitigation of model biases through parameter optimization when the assimilation model consists of a "biased" time-differencing. Here, the "biased" time-differencing is defined by a different time-differencing scheme from the "truth" model that is used to produce "observations", which generates different mean values, climatology and variability of the assimilation model from the "truth" model. A series of assimilation experiments is performed to explore the impact of parameter optimization on model bias mitigation and climate estimation, as well as the role of different media parameter estimations. While the stochastic "physics" implemented by perturbing parameters can enhance the ensemble spread significantly and improve the representation of the model ensemble, signal-enhanced parameter estimation is able to mitigate the model biases on mean values and climatology, thus further improving the accuracy of estimated climate states, especially for the low-frequency signals. In addition, in a multiple timescale coupled system, parameters pertinent to low-frequency components have more impact on climate signals. Results also suggest that deep ocean observations may be indispensable for improving the accuracy of climate estimation, especially for low-frequency signals.
Highlights
Imperfect dynamical core, empirical physical schemes and improper parameter values are several sources of couple model bias (Zhang et al, 2012)
While coupled model parameter estimation has shown a great potential to improve the quality of climate estimation and prediction as well as model simulation, the impact of imperfect dynamical cores such as imperfect numerical schemes has not been examined yet
To address the question, based on the data assimilation scheme with “enhancive” parameter correction (DAEPC) algorithm (Zhang et al, 2012), we study how to mitigate coupled model bias induced by imperfect time-differencing schemes through parameter optimization
Summary
Empirical physical schemes and improper parameter values are several sources of couple model bias (Zhang et al, 2012). To constrain model biases and improve the quality of climate estimation and prediction, Zhang et al (2012) designed a coupled data assimilation scheme with what these authors called “enhancive” parameter correction (DAEPC) based on an ensemble Kalman filter with the adjustment idea (Anderson, 2001). While coupled model parameter estimation has shown a great potential to improve the quality of climate estimation and prediction as well as model simulation, the impact of imperfect dynamical cores such as imperfect numerical schemes has not been examined yet. After describing the simple pycnocline prediction model and the method of ensemble coupled data assimilation for parameter estimation, two different time-differencing schemes are introduced and the setting of the biased twin experiment framework is discussed in Sect.
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