The climate system exhibits complex variability across different timescales. Part of this complexity is influenced by the teleconnections, recurring patterns in the atmosphere and ocean that strongly shape the regional climate variability and climate change. However, given the large internal variability and strong external forcings involved, understanding the role of teleconnections in climate variability and change remains challenging. Statistical, dynamical and modelling approaches have provided many insights to date. More recently, the integration of these approaches has developed rapidly, and new data-driven approaches are becoming widespread. This session aims to bring together researchers using any combination of these approaches to explore climate variability, teleconnections across timescales from synoptic scale to long-term change, and in particular, how variability on different timescales is connected. The physical explainability and interpretability of statistical and modelling results as well as the accurate and appropriate use of statistics in physics-centred research are a focus of the session.
We invite contributions that address one or more of the following topics: disentangling variability in teleconnections and their influence on regional climate, dynamics and predictive potential of teleconnections, the influence of large-scale changes in driving future regional climate change, understanding model-observation discrepancies in climate variability and teleconnections.
Studies that employ innovative approaches to bridge statistical analysis and physical understanding are particularly encouraged, including but not limited to machine learning techniques, causal inference methods, storyline approaches, Bayesian methods, and novel diagnostics for teleconnections.
We invite contributions that address one or more of the following topics: disentangling variability in teleconnections and their influence on regional climate, dynamics and predictive potential of teleconnections, the influence of large-scale changes in driving future regional climate change, understanding model-observation discrepancies in climate variability and teleconnections.
Studies that employ innovative approaches to bridge statistical analysis and physical understanding are particularly encouraged, including but not limited to machine learning techniques, causal inference methods, storyline approaches, Bayesian methods, and novel diagnostics for teleconnections.