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

Related authors

Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022,https://doi.org/10.5194/gmd-15-6115-2022, 2022
Short summary
Flow dependence of wintertime subseasonal prediction skill over Europe
Constantin Ardilouze, Damien Specq, Lauriane Batté, and Christophe Cassou
Weather Clim. Dynam., 2, 1033–1049, https://doi.org/10.5194/wcd-2-1033-2021,https://doi.org/10.5194/wcd-2-1033-2021, 2021
Short summary
Subseasonal-to-seasonal (S2S) forecasts with CNRM-CM: a case study on the July 2015 West-European heat wave
Constantin Ardilouze, Lauriane Batté, and Michel Déqué
Adv. Sci. Res., 14, 115–121, https://doi.org/10.5194/asr-14-115-2017,https://doi.org/10.5194/asr-14-115-2017, 2017
Short summary

Related subject area

Climate and Earth system modeling
FINAM is not a model (v1.0): a new Python-based model coupling framework
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025,https://doi.org/10.5194/gmd-18-4483-2025, 2025
Short summary
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025,https://doi.org/10.5194/gmd-18-4399-2025, 2025
Short summary
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025,https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025,https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025,https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary

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.
Download
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.
Share