Abstract
Abstract. Sea surface temperature is the key variable when tackling seasonal to decadal climate forecasts. 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. In addition, recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability and investigate possible teleconnections, the sea surface temperature based statistical seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of the S4CAST model whose operation is focused on studying the impacts of sea surface temperature on any climate-related variable. Two applications focused on analyzing the predictability of different climatic events have been implemented as benchmark examples.
Highlights
Global oceans have the capacity to store and release heat as energy that is transferred to the atmosphere altering global atmospheric circulation
Statistical models, despite being a useful and effective supplement, are mostly unable to reproduce the nonlinearity in the ocean–atmosphere system, exceptions include neural networks and Bayesian methods
Statistical models have evolved linked to dynamical models, either as an alternative or within them as a hybrid model
Summary
Global oceans have the capacity to store and release heat as energy that is transferred to the atmosphere altering global atmospheric circulation. Much research has been conducted to study the impacts of worldwide sea surface temperature anomalies (SSTA) by means of dynamical models, observational studies and statistical methods In this way, tropical oceans receive greater relevance (Rasmusson and Carpenter, 1982; Harrison and Larkin, 1998; Klein et al, 1999; Saravanan and Chang, 2000; Trenberth et al, 2002; Chang et al, 2006; Ding et al, 2012; Wang et al, 2012; Ham, 2013a, b; Keenlyside et al, 2013). These methods have been widely used in seasonal climate forecasting, either to complement dynamical models or to be applied independently In this way, Climate Predictability Tool (CPT) developed at International Research Institute for Climate and Society (IRI) allows the user to apply multivariate linear regression techniques (e.g., CCA) to get their own predictions (Korecha and Barnston, 2007; Recalde-Coronel et al, 2014; Barnston and Tippet, 2014).
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