This session invites contributions spanning all aspects of prediction, predictability, and applications on the Subseasonal-to-Seasonal (S2S) (i.e., 2 weeks to 2 months) lead time range. The session welcomes contributions on the following:
(a) Modes of variability (e.g. Madden Julian Oscillation (MJO) and others) impacting the S2S predictability;
(b) Tropical/extratropical wave dynamics and their effects on weather patterns;
(c) Teleconnections and combined influence of climate variability modes;
(d) The role of the atmosphere, ocean, land, and ice processes in S2S predictability;
(e) Predictability and predictive skill of atmospheric or surface variables, and other variables relevant for socio-economic sectors, such as sea ice, snow cover, soil moisture, and land surface;
(f) Use of AI/ML methods for S2S prediction, data-driven models, post-processing, and attribution, including innovative techniques for improving forecast accuracy;
(g) Case studies of extreme or high-impact event prediction on the S2S timescale;
(h) Sector-specific applications, impact studies, and climate services on the S2S timescale, including integration of S2S predictions into decision support systems at local, regional, or global levels and co-production of knowledge with stake-holders and decision-makers;
(i) Evaluation and improvement of S2S prediction systems, including advancements in model physics and comparison between dynamical and data-driven prediction models, data assimilation, ensemble forecasting, and initialization techniques.
(a) Modes of variability (e.g. Madden Julian Oscillation (MJO) and others) impacting the S2S predictability;
(b) Tropical/extratropical wave dynamics and their effects on weather patterns;
(c) Teleconnections and combined influence of climate variability modes;
(d) The role of the atmosphere, ocean, land, and ice processes in S2S predictability;
(e) Predictability and predictive skill of atmospheric or surface variables, and other variables relevant for socio-economic sectors, such as sea ice, snow cover, soil moisture, and land surface;
(f) Use of AI/ML methods for S2S prediction, data-driven models, post-processing, and attribution, including innovative techniques for improving forecast accuracy;
(g) Case studies of extreme or high-impact event prediction on the S2S timescale;
(h) Sector-specific applications, impact studies, and climate services on the S2S timescale, including integration of S2S predictions into decision support systems at local, regional, or global levels and co-production of knowledge with stake-holders and decision-makers;
(i) Evaluation and improvement of S2S prediction systems, including advancements in model physics and comparison between dynamical and data-driven prediction models, data assimilation, ensemble forecasting, and initialization techniques.