Probabilistic learning for predictive modeling of climate variability

While comprehensive climate models are skilful at predicting the response of the climate system to external forcing, they are less skilful when it comes to predicting the natural variability of climate. A variety of probabilistic machine learning techniques ranging from Reservoir Computing to Generative Adversarial Networks to Bayesian Neural Networks are considered in the latter context of predicting natural variability of climate. These models are seen to improve upon the Linear Inverse Modeling (LIM) approach which has itself been sometimes thought of as capturing the bulk of the predictable component of natural variability.