Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-1903-2020
https://doi.org/10.5194/gmd-13-1903-2020
Development and technical paper
 | 
16 Apr 2020
Development and technical paper |  | 16 Apr 2020

On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments

Colin Grudzien, Marc Bocquet, and Alberto Carrassi

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Cited articles

Arnold, H. M., Moroz, I. M., and Palmer, T. N.: Stochastic parametrizations and model uncertainty in the Lorenz'96 system, Phil. Trans. R. Soc. A, 371, 20110479, https://doi.org/10.1098/rsta.2011.0479, 2013. a
Berry, T. and Harlim, J.: Linear theory for filtering nonlinear multiscale systems with model error, Proc. R. Soc. A, 470, 20140168, https://doi.org/10.1098/rspa.2014.0168, 2014. a
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304504, https://doi.org/10.1080/16000870.2017.1304504, 2017. a
Bocquet, M., Gurumoorthy, K. S., Apte, A., Carrassi, A., Grudzien, C., and Jones, C. K. R. T.: Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace, SIAM/ASA J. Uncertainty Quantification, 5, 304–333, 2017. a
Boers, N., Chekroun, M. D., Liu, H., Kondrashov, D., Rousseau, D.-D., Svensson, A., Bigler, M., and Ghil, M.: Inverse stochastic–dynamic models for high-resolution Greenland ice core records, Earth Syst. Dynam., 8, 1171–1190, https://doi.org/10.5194/esd-8-1171-2017, 2017. a
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All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
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