The socio-economic impacts of weather phenomena pose a challenge to carbon-neutral development and highlight society's need for accurate weather forecasts and climate projections. For example, regional weather conditions directly affect renewables-based power systems by modulating power output and demand, and atmospheric extreme events can cause damage or failure of energy infrastructure.

Despite substantial progress in numerical modelling in recent decades, predictability for weather and extreme events is often limited and the assessments of future changes remain uncertain. This underscores the need to improve our understanding of the complex, nonlinear interactions of dynamical and physical processes that influence predictability at different lead times and determine the location, timing, and magnitude of extreme events.

This session will discuss our current understanding of how physical and dynamical processes connect atmospheric motions across temporal and spatial scales and how this relates to intrinsic and practical predictability of various weather phenomena. We particularly welcome contributions advancing our understanding, prediction, and future projections of weather and climate extremes, from both an applied and theoretical viewpoint, and with socio-economic impacts, e.g. on power systems.

Topics of interest include but are not limited to:


(1) Synoptic-scale atmospheric dynamics affecting the timing, positioning, and amplitude of weather events (e.g., the stationarity and amplitude of Rossby waves).
(2) Large-scale atmospheric and oceanic influences (e.g., the stratosphere, the Artic, or tropical oceans) on atmospheric variability and predictability in the midlatitudes.
(3) Intrinsic limits of predictability for various atmospheric phenomena and their link to the multi-scale, non-linear nature of atmospheric dynamics.
(4) Practical limits of predictability and the representation of atmospheric phenomena in numerical weather prediction and climate models including sensitivities to the model physics.
(5) Weather and climate extremes, including compound extreme events, their dynamics, predictability, and representation in weather and climate models.
(6) Statistical and mathematical approaches for the study of extreme events.
(7) Impact and risk assessment analyses of extreme events, in particular with a focus on renewable power systems and Europe.
(8) Extreme event attribution and changes in extreme event occurrences under climate change.