Predicting sea surface temperatures with coupled reservoir computers

Abstract

Abstract. Sea surface temperature (SST) is a key factor in understanding the greater climate of the Earth, and it is also an important variable when making weather predictions. Methods of machine learning have become ever more present and important in data-driven science and engineering, including in important areas for Earth science. Here, we propose an efficient framework that allows us to make global SST forecasts using a coupled reservoir computer method that we have specialized to this domain, allowing for template regions that accommodate irregular coastlines. Reservoir computing is an especially good method for forecasting spatiotemporally complex dynamical systems, as it is a machine learning method that, despite many randomly selected weights, is highly accurate and easy to train. Our approach provides the benefit of a simple and computationally efficient model that is able to predict SSTs across the entire Earth's oceans. The results are demonstrated to generally follow the actual dynamics of the system over a forecasting period of several weeks.