Calibrated forecasts of quasi‐periodic climate processes with deep echo state networks and penalized quantile regression

Abstract

AbstractAmong the most relevant processes in the Earth system for human habitability are quasi‐periodic, ocean‐driven multi‐year events whose dynamics are currently incompletely characterized by physical models, and hence poorly predictable. This work aims at showing how (1) data‐driven, stochastic machine learning approaches provide an affordable yet flexible means to forecast these processes; (2) the associated uncertainty can be properly calibrated with fast ensemble‐based approaches. While the methodology introduced and discussed in this work pertains to synoptic scale events, the principle of augmenting incomplete or highly sensitive physical systems with data‐driven models to improve predictability is far more general and can be extended to environmental problems of any scale in time or space.