Description
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
Table of Contents
- Introduction: why subseasonal-to-seasonal prediction?- Weather forecasting: what sets the forecast skill horizon?- Weather within climate: subseasonal predictability of tropical daily rainfall characteristics- The Madden–Julian oscillation- Extratropical subseasonal-to-seasonal oscillations and multiple regimes: the dynamical systems view- Tropical-extratropical interactions and teleconnections- Land surface processes relevant to subseasonal-to-seasonal prediction- The role of the ocean in subseasonal-to-seasonal predictability and prediction- The role of sea ice in subseasonal to seasonal predictability- Subseasonal predictability and the stratosphere- Forecast system design, configuration, and complexity- The THORPEX interactive grand global ensemble and subseasonal-to-seasonal ensembles- Forecast recalibration and multimodel combination- Forecast verification for subseasonal-to-seasonal timescales- Machine learning for subseasonal-to-seasonal prediction- Subseasonal-to-seasonal prediction of weather extremes- Pilot experiences in using seamless forecasts for early action: Ready–Set–Go approach in the Red Cross- Communication and dissemination of forecasts and engaging user communities- Subseasonal prediction of Indian summer monsoon variability and extreme weather events- Predicting climate impacts on health at subseasonal to seasonal timescales- Coproducing reliable, actionable subseasonal-to-seasonal climate services across Africa- Subseasonal to seasonal climate predictions for energy- Subseasonal to seasonal forecasts enhance effective marine decision-making in a fast-changing ocean- Epilogue
