Articles | Volume 8, issue 11
https://doi.org/10.5194/gmd-8-3639-2015
https://doi.org/10.5194/gmd-8-3639-2015
Model description paper
 | 
06 Nov 2015
Model description paper |  | 06 Nov 2015

S4CAST v2.0: sea surface temperature based statistical seasonal forecast model

R. Suárez-Moreno and B. Rodríguez-Fonseca

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Cited articles

Adams, R. M., Chen, C. C., McCarl, B. A., and Weiher, R. F.: The economic consequences of ENSO events for agriculture, Clim. Res., 13, 165–172, 1999.
Ault, T. R., Cole, J. E., and St George, S.: The amplitude of decadal to multidecadal variability in precipitation simulated by state-of-the-art climate models, Geophys. Res. Lett., 39, L21705, https://doi.org/10.1029/2012GL053424, 2012.
Baboo, S. S. and Shereef, I. K.: An efficient weather forecasting system using artificial neural network, International Journal of Environmental Science and Development, 1, 2010–0264, 2010.
Barnett, T. P.: Monte Carlo climate forecasting, J. Climate, 8, 1005–1022, 1995.
Barnett, T. P. and Preisendorfer, R.: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis, Mon. Weather Rev., 115, 1825–1850, 1987.
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The non-stationary links between sea surface temperature and global atmospheric circulation have served to create the S⁴CAST model. Here we describe the model, based on a statistical tool to be focused on the study of teleconnections and predictability of any climate-related variable that keeps a link with sea surface temperature. Due to its intuitive operation and free availability of the code, the model can be used both to supplement general circulation models and in a purely academic context.
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