Abstract
As stated, SST is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. As a starting point for this thesis, a statistical model was designed and created to improve the predictability and investigate potential non-stationary teleconnections. This model has been named as the sea surface temperature-based statistical seasonal foreCAST model (S4CAST). The model is based on the MCA method and introduces the novelty of considering the non-stationary links between the predictor and predictand fields. The results presented in this section are focused on the model development, integrating the methodologies described in Chap. 6. The model has been published (Suárez-Moreno and Rodríguez-Fonseca 2015), and the code is freely available online (see Sect. 7.6). The S4CAST model has been created and used throughout this thesis to put forward a series of hypothesis. Most of the results collected in this section correspond to the publication aforementioned.
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Suárez Moreno, R. (2019). A Statistical Model Based on Non-stationary Predictors. In: Interdecadal Changes in Ocean Teleconnections with the Sahel. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-99450-5_7
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