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

Related authors

Spy4Cast v1.0: a Python Tool for statistical seasonal forecast based on Maximum Covariance Analysis
Pablo Duran-Fonseca and Belén Rodríguez-Fonseca
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-164,https://doi.org/10.5194/gmd-2024-164, 2024
Preprint under review for GMD
Short summary
NN4CAST: An end-to-end deep learning application for seasonal climate forecasts
Víctor Galván Fraile, Belén Rodríguez-Fonseca, Irene Polo, Marta Martín-Rey, and María N. Moreno-García
EGUsphere, https://doi.org/10.5194/egusphere-2024-2897,https://doi.org/10.5194/egusphere-2024-2897, 2024
Preprint archived
Short summary

Related subject area

Climate and Earth system modeling
A Fortran–Python interface for integrating machine learning parameterization into earth system models
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025,https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025,https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025,https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025,https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025,https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary

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.
Download
Short summary
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.
Share