This session invites contributions spanning all aspects of prediction and predictability on the subseasonal (2 weeks to 2 months) forecasting timescale, also known as subseasonal-to-seasonal (S2S) prediction. We welcome interdisciplinary research that covers predictions, processes, early warning capabilities and which supports applications and decision-making across sectors (including, but not limited to, the examples listed below). In light of recent advances in artificial intelligence (AI) and machine learning (ML) techniques for subseasonal prediction, contributions on AI/ML model developments, benchmarking frameworks and applications are very welcome. Of special interest are contributions related to the AI Weather Quest, an open international competition benchmarking AI-based subseasonal forecasts in real-time.
Physical drivers and processes
-Role of the atmosphere, ocean, land, and ice processes in extended-range/S2S predictability;
-Modes of variability (e.g., Madden Julian Oscillation (MJO), quasi-biennial oscillation (QBO), polar vortex strength, and others) impacting the extended-range/S2S predictability;
-Impact of global warming on early warning systems, changes in risks.
Prediction systems
-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;
-Use of AI/ML methods for S2S prediction, data-driven models, post-processing, and attribution, including innovative techniques for improving forecast accuracy.
Extreme events and early warnings
-Early warnings for single- and multi-hazard events;
-Sources of predictability for extreme events, including multi-hazards events, on the S2S timescale (including driver identification and teleconnections);
-Case studies of extreme or high-impact event prediction and impacts on early warnings;
-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.
Applications and societal relevance
-Sector-specific applications, impact studies on the S2S/extended range timescale;
-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.