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.
1. Chapter 1. Introduction: why subseasonal-to-seasonal prediction? - p. 1 2. Chapter 2. Weather forecasting: what sets the forecast skill horizon? - p. 21 3. Chapter 3. Weather within climate: subseasonal predictability of tropical daily rainfall characteristics - p. 69 4. Chapter 4. The Madden–Julian oscillation - p. 97 5. Chapter 5. Extratropical subseasonal-to-seasonal oscillations and multiple regimes: the dynamical systems view - p. 137 6. Chapter 6. Tropical-extratropical interactions and teleconnections - p. 181 7. Chapter 7. Land surface processes relevant to subseasonal-to-seasonal prediction - p. 225 8. Chapter 8. The role of the ocean in subseasonal-to-seasonal predictability and prediction - p. 271 9. Chapter 9. The role of sea ice in subseasonal to seasonal predictability - p. 321 10. Chapter 10. Subseasonal predictability and the stratosphere - p. 355 11. Chapter 11. Forecast system design, configuration, and complexity - p. 399 12. Chapter 12. The THORPEX interactive grand global ensemble and subseasonal-to-seasonal ensembles - p. 421 13. Chapter 13. Forecast recalibration and multimodel combination - p. 477 14. Chapter 14. Forecast verification for subseasonal-to-seasonal timescales - p. 503 15. Chapter 15. Machine learning for subseasonal-to-seasonal prediction - p. 539 16. Chapter 16. Subseasonal-to-seasonal prediction of weather extremes - p. 591 17. Chapter 17. Pilot experiences in using seamless forecasts for early action: Ready–Set–Go approach in the Red Cross - p. 633 18. Chapter 18. Communication and dissemination of forecasts and engaging user communities - p. 647 19. Chapter 19. Subseasonal prediction of Indian summer monsoon variability and extreme weather events - p. 677 20. Chapter 20. Predicting climate impacts on health at subseasonal-to-seasonal timescales - p. 721 21. Chapter 21. Coproducing reliable, actionable subseasonal-to-seasonal climate services across Africa - p. 753 22. Chapter 22. Subseasonal-to-seasonal climate predictions for energy - p. 779 23. Chapter 23. Subseasonal-to-seasonal forecasts enhance effective marine decision-making in a fast-changing ocean - p. 809 24. Chapter 24. Epilogue - p. 833 25. Index - p. 837
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.
1. Chapter 1. Introduction: why subseasonal-to-seasonal prediction? - p. 1 2. Chapter 2. Weather forecasting: what sets the forecast skill horizon? - p. 21 3. Chapter 3. Weather within climate: subseasonal predictability of tropical daily rainfall characteristics - p. 69 4. Chapter 4. The Madden–Julian oscillation - p. 97 5. Chapter 5. Extratropical subseasonal-to-seasonal oscillations and multiple regimes: the dynamical systems view - p. 137 6. Chapter 6. Tropical-extratropical interactions and teleconnections - p. 181 7. Chapter 7. Land surface processes relevant to subseasonal-to-seasonal prediction - p. 225 8. Chapter 8. The role of the ocean in subseasonal-to-seasonal predictability and prediction - p. 271 9. Chapter 9. The role of sea ice in subseasonal to seasonal predictability - p. 321 10. Chapter 10. Subseasonal predictability and the stratosphere - p. 355 11. Chapter 11. Forecast system design, configuration, and complexity - p. 399 12. Chapter 12. The THORPEX interactive grand global ensemble and subseasonal-to-seasonal ensembles - p. 421 13. Chapter 13. Forecast recalibration and multimodel combination - p. 477 14. Chapter 14. Forecast verification for subseasonal-to-seasonal timescales - p. 503 15. Chapter 15. Machine learning for subseasonal-to-seasonal prediction - p. 539 16. Chapter 16. Subseasonal-to-seasonal prediction of weather extremes - p. 591 17. Chapter 17. Pilot experiences in using seamless forecasts for early action: Ready–Set–Go approach in the Red Cross - p. 633 18. Chapter 18. Communication and dissemination of forecasts and engaging user communities - p. 647 19. Chapter 19. Subseasonal prediction of Indian summer monsoon variability and extreme weather events - p. 677 20. Chapter 20. Predicting climate impacts on health at subseasonal-to-seasonal timescales - p. 721 21. Chapter 21. Coproducing reliable, actionable subseasonal-to-seasonal climate services across Africa - p. 753 22. Chapter 22. Subseasonal-to-seasonal climate predictions for energy - p. 779 23. Chapter 23. Subseasonal-to-seasonal forecasts enhance effective marine decision-making in a fast-changing ocean - p. 809 24. Chapter 24. Epilogue - p. 833 25. Index - p. 837
