Unravelling Climate Variability and Teleconnections Across Time Scales

Convener: Julia MindlinECSECS | Co-conveners: Giuseppe Zappa, Xiaocen ShenECSECS, Rohit GhoshECSECS, Camille Li

The climate system exhibits complex variability across multiple timescales, from extreme weather events to long-term change. A key component of this complexity arises from teleconnections - recurring patterns in the atmosphere and ocean that strongly influence regional climate signals. These teleconnections may be linked to periodic modes of variability (ENSO, IOD, QBO, AMV, PDV, etc.) or to responses driven by anthropogenic forcing (e.g. tropical Pacific warming pattern, North Atlantic warming hole, sea ice loss, etc.). But disentangling the origin and regional impacts of teleconnections is challenging due to the interplay between internal variability and external forcing. Statistical, dynamical, and advanced modelling approaches have already provided many insights, and are now increasingly integrated with data-driven methods. This session aims to bring together researchers applying any combination of these approaches to investigate teleconnections and their role in driving climate variability and change across timescales, particularly how variability on different timescales is connected.

We welcome contributions addressing one or more of the following themes:
disentangling variability in teleconnections and their influence on regional climate, including their dynamics and predictive potential;
assessing the role of large-scale circulation changes in driving future regional climate change,
understanding discrepancies between simulated and observed climate variability and teleconnections, including potential improvements arising from advances in model resolution, process representation and emulators.
understanding changes in teleconnection patterns arising from strong external forcing.

This session emphasizes the physical interpretability of statistical and modelling results along with the accurate, context-appropriate use of statistical tools in physics-centered climate research. Studies that employ innovative methods to bridge statistical analysis and physical understanding – such as machine learning, causal inference methods, storyline approaches, Bayesian framework, or novel diagnostics for teleconnections – are encouraged.