Inter-Basin Dynamics: Cross-Ocean Interactions and Decadal Forecasting

This Collection supports and amplifies research related to        SDG 13 - Climate Action

Understanding the complex interactions between ocean basins is critical for advancing decadal climate forecasting and unraveling the mechanisms driving global climate variability. It is of prime importance to explore the dynamic interplay between inter-basin processes, focusing on phenomena such as the Atlantic Niño, El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and their causal connections mediated by atmospheric bridges. It investigates how multidecadal variability in the Atlantic and Pacific Oceans modulates regional and global climate patterns, influencing extreme weather events, hydrological cycles, and long-term climate trends.

This Collection aims to enhance the accuracy of decadal-scale climate projections and inform strategies for climate resilience, by addressing climate dynamics, ocean-atmosphere interactions, and the application of advanced numerical methods (e.g., machine learning and artificial intelligence). Contributions will bridge observational analyses, theoretical frameworks, and cutting-edge dynamical and statistical modeling to decode the causality of cross-basin teleconnections and their impacts on weather extremes, hydrological processes, and marine ecosystems, offering insights critical for policymakers and practitioners in adapting to a changing climate.

This Collection welcomes Original Research articles as well as Reviews and Perspectives that underscore the following themes, which include, but are not limited to:

• Mechanisms and causality of teleconnections between ENSO, Atlantic Niño, IOD, and other climate modes
• Role of atmospheric bridges in transferring energy and anomalies across ocean basins
• Impacts of Atlantic and Pacific Multidecadal Variability on regional climates and extreme weather events
• Advancements in decadal prediction systems and probabilistic forecasting techniques
• Applications of machine learning and artificial intelligence to disentangle complex interactions and enhance forecasting accuracy
• Historical and paleoclimate perspectives on inter-basin dynamics and their evolution under climate change