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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 2
Nonlin. Processes Geophys., 21, 357–366, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 21, 357–366, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 07 Mar 2014

Research article | 07 Mar 2014

Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model

G.-J. Han1, X.-F. Zhang1, S. Zhang2, X.-R. Wu1, and Z. Liu3,4 G.-J. Han et al.
  • 1Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China
  • 2GFDL/NOAA, Princeton University, Princeton, NJ, USA
  • 3Nelson Institute Center for Climatic Research and the Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WS, USA
  • 4Laboratory for Climate, Ocean and Atmosphere Studies, Peking University, Beijing, China

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.

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