Subseasonal-to-Seasonal Prediction provides the latest thinking from experts in the fields of sub-seasonal to seasonal (S2S) predictability science, numerical modeling, operational forecasting, and developing application sectors. This fully updated second edition comprehensively covers the sources of S2S predictability, S2S modeling, and forecasting using dynamical or machine learning methods, and S2S applications. There are brand new chapters on the role of the ocean in sub-seasonal predictability, machine learning in S2S prediction, co-produced S2S climate services in Africa, S2S for energy, and marine weather prediction on S2S timescales. This valuable resource offers atmospheric and climate scientists the very latest developments in this rapidly evolving field. - Contributed chapters from experts in S2S science and forecasting updated for use in an emerging and interdisciplinary field- Synthesis of the state of S2S science, through the use of concrete examples that enable potential users of S2S forecasts to quickly grasp the potential for use in their own decision setting- Broad set of interdisciplinary linked topics, illustrated with graphic examples to powerfully illustrate the interdisciplinary linkages
