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Global warming or climate change is probably the current greatest scientific challenge that mankind faces. From all both natural and anthropogenic factors involved in global warming, as well as their complex interrelations, few attention has focused on those factors external to Earth, as the solar variability. In this work we present a hybrid system in artificial intelligence based on artificial neural networks and empirical mode decomposition to determine the interrelation between total solar irradiance received by Earth in the last four decades with a key index in climate change: oceanic surface temperature. The results of this ongoing research show an evident interrelation between both indexes, and strongly suggest that the main external factor driving of the variations in the oceanic surface temperature are the variations in the input solar energy of Earth.

Sistema híbrido basado en redes neuronales artificiales y descomposición modal empírica para la evaluación de la interrelación entre la irradiancia solar total y el calentamiento global