Predicting weather and climate fluctuations at sub-seasonal to seasonal (S2S) time scales is of high relevance for society, in the current context of rapid climate changes.
Rapid progress in this emerging area of research has been possible thanks to an improved understanding of physical processes underpinning predictability, the sustained development of prediction systems and observational networks, as well as and the advent of high-performance computing. In that sense, APPLICATE is a prominent example of how the scientific community addresses the S2S prediction challenge.
The scientific literature on S2S prediction has flourished in recent years, and it appears difficult to have a broad and synthetic view on the current state on knowledge. In addition, S2S prediction is a multi-faceted research area using concepts from mathematics and statistics (data assimilation, bias correction, forecast verification, ensemble forecasting), physics (atmospheric dynamics, predictability mechanisms, teleconnections), computational sciences and even social sciences, when it comes to communicating the climate information in meaningful ways. To embrace and synthesize all this knowledge, a book [https://www.elsevier.com/books/sub-seasonal-to-seasonal-prediction/robertson/978-0-12-811714-9] "Sub-seasonal to Seasonal Prediction" has recently been published (Eds A. Robertson and F. Vitart). The book is an accessible yet rigorous synthesis of what is known on S2S prediction, and will quickly become a reference for students, teachers and researchers all alike.
Five APPLICATE scientists (Matthieu Chevallier, François Massonnet, Helge Goessling, Virginie Guemas, Thomas Jung) were solicited to write a chapter on "The Role of Sea Ice in Sub-seasonal Predictability". In this chapter, the main sources of Arctic and Antarctic sea ice predictability are first reviewed. Then, the authors review the current sea ice forecasting capabilities and their limits. Finally, evidence is presented that sea ice can also be seen as a source of S2S predictability for the polar and extra-polar atmosphere. The chapter highlights the central role that sea ice is playing on S2S predictability in polar regions and beyond.
This contribution is a recognition of the authors individual and collective leaderships in the field of polar prediction. To a larger extent, it also underlines that global S2S prediction systems will have to account for the rapidly changing conditions happening at the poles, a notion that has been at the heart of APPLICATE since its inception.