Articles | Volume 9, issue 6
https://doi.org/10.5194/gmd-9-2055-2016
https://doi.org/10.5194/gmd-9-2055-2016
Model evaluation paper
 | 
07 Jun 2016
Model evaluation paper |  | 07 Jun 2016

Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

Lauriane Batté and Michel Déqué

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Cited articles

Alessandri, A., Borrelli, A., Navarra, A., Arribas, A., Déqué, M., Rogel, P., and Weisheimer, A.: Evaluation of probabilistic quality and value of the ENSEMBLES multimodel seasonal forecasts: comparison with DEMETER, Mon. Weather Rev., 139, 581–607, https://doi.org/10.1175/2010MWR3417.1, 2011.
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF ocean reanalysis system ORAS4, Q. J. Roy. Meteor. Soc., 139, 1132–1161, https://doi.org/10.1002/qj.2063, 2013.
Barreiro, M. and Chang, P.: A linear tendency correction technique for improving seasonal prediction of SST, Geophys. Res. Lett., 31, L23209, https://doi.org/10.1029/2004GL021148, 2004.
Batté, L. and Déqué, M.: Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts, Tellus, 63A, 283–299, https://doi.org/10.1111/j.1600-0870.2010.00493.x, 2011.
Batté, L. and Déqué, M.: A stochastic method for improving seasonal predictions, Geophys. Res. Lett., 39, L09707, https://doi.org/10.1029/2012GL051406, 2012.
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Short summary
Taking into account model inadequacies is a key challenge in climate forecasting. As part of the FP7-SPECS project, we examine how stochastic perturbations of atmospheric model dynamics impact seasonal forecast quality of the CNRM coupled model. The method described in this paper helps derive model error statistics as well as improve key aspects of our forecasting system such as systematic errors over the North Atlantic mid-latitudes.