NN4CAST: An end-to-end deep learning application for seasonal climate forecasts

Abstract. Predicting climate variables at seasonal time scales is crucial for climate services. At these time scales, the most important driver of the atmospheric variability patterns is the ocean, which can trigger local perturbations that could affect the climate of remote areas through different teleconnection mechanisms. Dynamical models are not always effective in overcoming the challenges posed by complex climate processes and may show a low signal-to-noise ratio in the response of some phenomena (such as ENSO). Recently, statistical approaches, which focus on the relationships between predictor and predictand fields, have gained popularity due to advances in computing capabilities and the availability of extensive meteorological datasets. This is particularly evidenced in the development of short-range weather prediction data-driven models. However, seasonal prediction remains challenging, especially in regions where there are complex non-linear remote interactions of non-stationary and seasonal dependent signals from different sources of predictability of the Earth system. For this reason, we have developed a non-linear modelling tool, named as Neural Network foreCAST (NN4CAST). It is a deep learning model library which provides a versatile tool for operational prediction and non-linear statistical analysis, which can enhance seasonal forecast accuracy and understanding of the underlying dynamics. It is demonstrated how NN4CAST is able to provide accurate seasonal predictions in regions where the atmospheric response to the ocean is mostly linear (i.e., tropics) as well as in remote areas through atmospheric teleconnections, where the signal is highly non-linear (i.e., North Atlantic). It is in those remote regions where the dynamical models fail to capture the climate signal, and where the deep learning models may be more useful for a seasonal data-driven or hybrid prediction system. Therefore, the application of this model could provide accurate and reliable forecasts that can be useful for the different strategic societal sectors such as marine ecosystems, health or energy, which require predictions on these time scales.