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Among the most relevant processes in the Earth system for human habitabilityare quasi-periodic, ocean-driven multi-year events whose dynamics are currentlyincompletely characterized by physical models, and hence poorly predictable.This work aims at showing how 1) data-driven, stochastic machine learningapproaches provide an affordable yet flexible means to forecast theseprocesses; 2) the associated uncertainty can be properly calibrated with fastensemble-based approaches. While the methodology introduced and discussed inthis work pertains to synoptic scale events, the principle of augmentingincomplete or highly sensitive physical systems with data-driven models toimprove predictability is far more general and can be extended to environmentalproblems of any scale in time or space.

Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression